diff --git a/spark/src/main/scala/org/apache/comet/serde/arrays.scala b/spark/src/main/scala/org/apache/comet/serde/arrays.scala
index 2f89b0e2e3..c66d53fd84 100644
--- a/spark/src/main/scala/org/apache/comet/serde/arrays.scala
+++ b/spark/src/main/scala/org/apache/comet/serde/arrays.scala
@@ -508,6 +508,28 @@ object CometCreateArray extends CometExpressionSerde[CreateArray] {
       return exprToProtoInternal(emptyArrayLiteral, inputs, binding)
     }
 
+    // DataFusion's `make_array` asserts strict element-type equality in
+    // `MutableArrayData::with_capacities` and panics on a mismatch. Spark's CreateArray is more
+    // permissive: its type coercion compares element types with `sameType`, which ignores
+    // nullability, so children that share a surface type but differ only in nested field
+    // nullability get no unifying cast. DataFusion tolerates container nullability differences
+    // (an `ArrayType.containsNull` / `MapType.valueContainsNull` mismatch is coerced), but NOT a
+    // struct field's nullability -- `array(struct(a not null), struct(a nullable))` panics inside
+    // `make_array_inner`. Decline only those cases (i.e. children that still differ after
+    // normalizing container nullability) so Spark's evaluator handles them.
+    //
+    // TODO: remove this decline once apache/datafusion#22366 lands; the upstream fix widens the
+    // element type via nullability-OR-merge and casts each child before MutableArrayData.
+    val normalizedTypes = children.map(c => normalizeContainerNullability(c.dataType))
+    if (normalizedTypes.distinct.size > 1) {
+      withFallbackReason(
+        expr,
+        "CreateArray children have mismatched data types: " +
+          children.map(_.dataType).distinct.mkString(", "),
+        children: _*)
+      return None
+    }
+
     val childExprs = children.map(exprToProtoInternal(_, inputs, binding))
 
     if (childExprs.forall(_.isDefined)) {
@@ -517,6 +539,26 @@ object CometCreateArray extends CometExpressionSerde[CreateArray] {
       None
     }
   }
+
+  /**
+   * Rewrites a type so that container nullability (`ArrayType.containsNull`,
+   * `MapType.valueContainsNull`) is forced to `true` everywhere, while struct field nullability
+   * is left intact. Two CreateArray children whose types differ ONLY in container nullability are
+   * tolerated by DataFusion's `make_array` (coerced), so they normalize equal here; a difference
+   * in a struct field's nullability survives normalization and triggers the decline above.
+   */
+  private def normalizeContainerNullability(dt: DataType): DataType = dt match {
+    case ArrayType(elementType, _) =>
+      ArrayType(normalizeContainerNullability(elementType), containsNull = true)
+    case MapType(keyType, valueType, _) =>
+      MapType(
+        normalizeContainerNullability(keyType),
+        normalizeContainerNullability(valueType),
+        valueContainsNull = true)
+    case StructType(fields) =>
+      StructType(fields.map(f => f.copy(dataType = normalizeContainerNullability(f.dataType))))
+    case other => other
+  }
 }
 
 object CometGetArrayItem extends CometExpressionSerde[GetArrayItem] {
diff --git a/spark/src/test/scala/org/apache/comet/CometArrayExpressionSuite.scala b/spark/src/test/scala/org/apache/comet/CometArrayExpressionSuite.scala
index 1c83a5b03f..ee0577a4e9 100644
--- a/spark/src/test/scala/org/apache/comet/CometArrayExpressionSuite.scala
+++ b/spark/src/test/scala/org/apache/comet/CometArrayExpressionSuite.scala
@@ -1113,4 +1113,43 @@ class CometArrayExpressionSuite extends CometTestBase with AdaptiveSparkPlanHelp
       }
     }
   }
+
+  test("array of structs with nullability-divergent children") {
+    // Spark's type coercion compares element types with `sameType`, which ignores nullability,
+    // so two struct children that differ ONLY in a nested field's nullability get no unifying
+    // cast -- CreateArray keeps children of different StructTypes. DataFusion's make_array asserts
+    // strict element-type equality (down to nested nullability) and panics on the mismatch. Comet
+    // must decline this CreateArray so Spark's evaluator handles it.
+    withParquetTable((0 until 5).map(i => (i, i.toLong)), "tbl") {
+      val df = spark
+        .table("tbl")
+        .select(
+          array(
+            // ct is NOT NULL (literal)
+            struct(col("_1").as("id"), lit("a").as("ct")),
+            // ct is NULLABLE (when without otherwise) -- same type, different nullability
+            struct(col("_1").as("id"), when(col("_1") === 0, lit("b")).as("ct"))).as("arr"))
+      checkSparkAnswerAndFallbackReason(df, "CreateArray children have mismatched data types")
+    }
+  }
+
+  test("array of maps with nullability-divergent struct values") {
+    // Same nested-nullability divergence as the struct case, but wrapped in a MapType value so we
+    // exercise normalizeContainerNullability's MapType branch: the two map children share a surface
+    // type and differ only in a nested struct field's nullability, so they survive container
+    // (`MapType.valueContainsNull`) normalization as distinct types and CreateArray must still
+    // decline -- DataFusion's make_array would otherwise panic on the struct-field mismatch.
+    withParquetTable((0 until 5).map(i => (i, i.toLong)), "tbl") {
+      val df = spark
+        .table("tbl")
+        .select(
+          array(
+            // map value struct has ct NOT NULL (literal)
+            map(lit("k"), struct(col("_1").as("id"), lit("a").as("ct"))),
+            // map value struct has ct NULLABLE -- same type, different nested nullability
+            map(lit("k"), struct(col("_1").as("id"), when(col("_1") === 0, lit("b")).as("ct"))))
+            .as("arr"))
+      checkSparkAnswerAndFallbackReason(df, "CreateArray children have mismatched data types")
+    }
+  }
 }