[SPARK-56343][SQL][TESTS] Add MERGE INTO test for type mismatch without schema evolution trigger condition

sql/core/src/test/scala/org/apache/spark/sql/connector/MergeIntoSchemaEvolutionTypeWideningAndExtraFieldTests.scala

@@ -144,6 +144,94 @@ trait MergeIntoSchemaEvolutionTypeWideningAndExtraFieldTests
       (3, 75, "newdep")).toDF("pk", "salary", "dep")
   )
 
+  // When assigning s.bonus to existing t.salary and source.salary has a wider type (long) than
+  // target.salary (int), no evolution should occur because the assignment uses s.bonus, not
+  // s.salary. The type mismatch on the same-named column should be irrelevant.
+  testEvolution("source has extra column with type mismatch on existing column -" +
+    "should not evolve when assigning from differently named source column")(
+    targetData = {
+      val schema = StructType(Seq(
+        StructField("pk", IntegerType, nullable = false),
+        StructField("salary", IntegerType),
+        StructField("dep", StringType)
+      ))
+      spark.createDataFrame(spark.sparkContext.parallelize(Seq(
+        Row(1, 100, "hr"),
+        Row(2, 200, "software")
+      )), schema)
+    },
+    sourceData = {
+      val schema = StructType(Seq(
+        StructField("pk", IntegerType, nullable = false),
+        StructField("salary", LongType),
+        StructField("dep", StringType),
+        StructField("bonus", LongType)
+      ))
+      spark.createDataFrame(spark.sparkContext.parallelize(Seq(
+        Row(2, 150L, "dummy", 50L),
+        Row(3, 250L, "dummy", 75L)
+      )), schema)
+    },
+    clauses = Seq(
+      update(set = "salary = s.bonus"),
+      insert(values = "(pk, salary, dep) VALUES (s.pk, s.bonus, 'newdep')")
+    ),
+    expected = Seq(
+      (1, 100, "hr"),
+      (2, 50, "software"),
+      (3, 75, "newdep")).toDF("pk", "salary", "dep"),
+    expectedWithoutEvolution = Seq(
+      (1, 100, "hr"),
+      (2, 50, "software"),
+      (3, 75, "newdep")).toDF("pk", "salary", "dep"),
+    expectedSchema = StructType(Seq(
+      StructField("pk", IntegerType, nullable = false),
+      StructField("salary", IntegerType),
+      StructField("dep", StringType)
+    )),
+    expectedSchemaWithoutEvolution = StructType(Seq(
+      StructField("pk", IntegerType, nullable = false),
+      StructField("salary", IntegerType),
+      StructField("dep", StringType)
+    ))
+  )
+
+  // When assigning s.bonus (StringType) to target salary (IntegerType), the types are
+  // incompatible. This should fail both with and without schema evolution because the explicit
+  // assignment has mismatched types regardless of evolution.
+  testEvolution("source has extra column with type mismatch on existing column -" +
+    "should fail when assigning from incompatible source column")(
+    targetData = {
+      val schema = StructType(Seq(
+        StructField("pk", IntegerType, nullable = false),
+        StructField("salary", IntegerType),
+        StructField("dep", StringType)
+      ))
+      spark.createDataFrame(spark.sparkContext.parallelize(Seq(
+        Row(1, 100, "hr"),
+        Row(2, 200, "software")
+      )), schema)
+    },
+    sourceData = {
+      val schema = StructType(Seq(
+        StructField("pk", IntegerType, nullable = false),
+        StructField("salary", LongType),
+        StructField("dep", StringType),
+        StructField("bonus", StringType)
+      ))
+      spark.createDataFrame(spark.sparkContext.parallelize(Seq(
+        Row(2, 150L, "dummy", "fifty"),
+        Row(3, 250L, "dummy", "seventy-five")
+      )), schema)
+    },
+    clauses = Seq(
+      update(set = "salary = s.bonus"),
+      insert(values = "(pk, salary, dep) VALUES (s.pk, s.bonus, 'newdep')")
+    ),
+    expectErrorContains = "Cannot safely cast",
+    expectErrorWithoutEvolutionContains = "Cannot safely cast"
+  )
+
   // No evolution when using named_struct to construct value without referencing new field
   testNestedStructsEvolution("source has extra struct field -" +
     "no evolution when not directly referencing new field - INSERT")(