[SPARK-36854][SQL] Handle ANSI intervals by the off-heap column vector

### What changes were proposed in this pull request?
Modify `OffHeapColumnVector.reserveInternal` to handle ANSI intervals - `DayTimeIntervalType` and `YearMonthIntervalType`.

### Why are the changes needed?
The changes fix the issue which the example below demonstrates:
```scala
scala> spark.conf.set("spark.sql.columnVector.offheap.enabled", true)
scala> spark.read.parquet("/Users/maximgekk/tmp/parquet_offheap").show()
21/09/25 22:09:03 ERROR Executor: Exception in task 0.0 in stage 3.0 (TID 3)
java.lang.RuntimeException: Unhandled YearMonthIntervalType(0,1)
	at org.apache.spark.sql.execution.vectorized.OffHeapColumnVector.reserveInternal(OffHeapColumnVector.java:562)
```
SPARK-36854 shows how the parquet files in `/Users/maximgekk/tmp/parquet_offheap` were prepared.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
By running the modified test suite:
```
$ build/sbt "sql/test:testOnly *ParquetIOSuite"
```

Closes apache#34106 from MaxGekk/ansi-interval-OffHeapColumnVector.

Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>

sql/core/src/main/java/org/apache/spark/sql/execution/vectorized/OffHeapColumnVector.java

@@ -550,11 +550,12 @@ protected void reserveInternal(int newCapacity) {
     } else if (type instanceof ShortType) {
       this.data = Platform.reallocateMemory(data, oldCapacity * 2L, newCapacity * 2L);
     } else if (type instanceof IntegerType || type instanceof FloatType ||
-        type instanceof DateType || DecimalType.is32BitDecimalType(type)) {
+        type instanceof DateType || DecimalType.is32BitDecimalType(type) ||
+        type instanceof YearMonthIntervalType) {
       this.data = Platform.reallocateMemory(data, oldCapacity * 4L, newCapacity * 4L);
     } else if (type instanceof LongType || type instanceof DoubleType ||
         DecimalType.is64BitDecimalType(type) || type instanceof TimestampType ||
-        type instanceof TimestampNTZType) {
+        type instanceof TimestampNTZType || type instanceof DayTimeIntervalType) {
       this.data = Platform.reallocateMemory(data, oldCapacity * 8L, newCapacity * 8L);
     } else if (childColumns != null) {
       // Nothing to store.

sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala

@@ -1062,20 +1062,24 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSparkSession
     }
   }
 
-  test("SPARK-36825: year-month/day-time intervals written and read as INT32/INT64") {
-    Seq(
-      YearMonthIntervalType() -> ((i: Int) => Period.of(i, i, 0)),
-      DayTimeIntervalType() -> ((i: Int) => Duration.ofDays(i).plusSeconds(i))
-    ).foreach { case (it, f) =>
-      val data = (1 to 10).map(i => Row(i, f(i)))
-      val schema = StructType(Array(StructField("d", IntegerType, false),
-        StructField("i", it, false)))
-      withTempPath { file =>
-        val df = spark.createDataFrame(sparkContext.parallelize(data), schema)
-        df.write.parquet(file.getCanonicalPath)
-        withAllParquetReaders {
-          val df2 = spark.read.parquet(file.getCanonicalPath)
-          checkAnswer(df2, df.collect().toSeq)
+  test("SPARK-36825, SPARK-36854: year-month/day-time intervals written and read as INT32/INT64") {
+    Seq(false, true).foreach { offHeapEnabled =>
+      withSQLConf(SQLConf.COLUMN_VECTOR_OFFHEAP_ENABLED.key -> offHeapEnabled.toString) {
+        Seq(
+          YearMonthIntervalType() -> ((i: Int) => Period.of(i, i, 0)),
+          DayTimeIntervalType() -> ((i: Int) => Duration.ofDays(i).plusSeconds(i))
+        ).foreach { case (it, f) =>
+          val data = (1 to 10).map(i => Row(i, f(i)))
+          val schema = StructType(Array(StructField("d", IntegerType, false),
+            StructField("i", it, false)))
+          withTempPath { file =>
+            val df = spark.createDataFrame(sparkContext.parallelize(data), schema)
+            df.write.parquet(file.getCanonicalPath)
+            withAllParquetReaders {
+              val df2 = spark.read.parquet(file.getCanonicalPath)
+              checkAnswer(df2, df.collect().toSeq)
+            }
+          }
         }
       }
     }