[SPARK-39135][SQL] DS V2 aggregate partial push-down should supports group by without aggregate functions

### What changes were proposed in this pull request?
Currently, the SQL show below not supported by DS V2 aggregate partial push-down.
`select key from tab group by key`

### Why are the changes needed?
Make DS V2 aggregate partial push-down supports group by without aggregate functions.

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

### How was this patch tested?
New tests

Closes apache#36492 from beliefer/SPARK-39135.

Authored-by: Jiaan Geng <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>

Author: beliefer

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/V2ScanRelationPushDown.scala

@@ -286,7 +286,7 @@ object V2ScanRelationPushDown extends Rule[LogicalPlan] with PredicateHelper wit
   private def supportPartialAggPushDown(agg: Aggregation): Boolean = {
     // We don't know the agg buffer of `GeneralAggregateFunc`, so can't do partial agg push down.
     // If `Sum`, `Count`, `Avg` with distinct, can't do partial agg push down.
-    agg.aggregateExpressions().exists {
+    agg.aggregateExpressions().isEmpty || agg.aggregateExpressions().exists {
       case sum: Sum => !sum.isDistinct
       case count: Count => !count.isDistinct
       case avg: Avg => !avg.isDistinct

sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCV2Suite.scala

@@ -670,6 +670,57 @@ class JDBCV2Suite extends QueryTest with SharedSparkSession with ExplainSuiteHel
     checkAnswer(df, Seq(Row(5)))
   }
 
+  test("scan with aggregate push-down: GROUP BY without aggregate functions") {
+    val df = sql("select name FROM h2.test.employee GROUP BY name")
+    checkAggregateRemoved(df)
+    checkPushedInfo(df,
+      "PushedAggregates: [], PushedFilters: [], PushedGroupByExpressions: [NAME],")
+    checkAnswer(df, Seq(Row("alex"), Row("amy"), Row("cathy"), Row("david"), Row("jen")))
+
+    val df2 = spark.read
+      .option("partitionColumn", "dept")
+      .option("lowerBound", "0")
+      .option("upperBound", "2")
+      .option("numPartitions", "2")
+      .table("h2.test.employee")
+      .groupBy($"name")
+      .agg(Map.empty[String, String])
+    checkAggregateRemoved(df2, false)
+    checkPushedInfo(df2,
+      "PushedAggregates: [], PushedFilters: [], PushedGroupByExpressions: [NAME],")
+    checkAnswer(df2, Seq(Row("alex"), Row("amy"), Row("cathy"), Row("david"), Row("jen")))
+
+    val df3 = sql("SELECT CASE WHEN SALARY > 8000 AND SALARY < 10000 THEN SALARY ELSE 0 END as" +
+      " key FROM h2.test.employee GROUP BY key")
+    checkAggregateRemoved(df3)
+    checkPushedInfo(df3,
+      """
+        |PushedAggregates: [],
+        |PushedFilters: [],
+        |PushedGroupByExpressions:
+        |[CASE WHEN (SALARY > 8000.00) AND (SALARY < 10000.00) THEN SALARY ELSE 0.00 END],
+        |""".stripMargin.replaceAll("\n", " "))
+    checkAnswer(df3, Seq(Row(0), Row(9000)))
+
+    val df4 = spark.read
+      .option("partitionColumn", "dept")
+      .option("lowerBound", "0")
+      .option("upperBound", "2")
+      .option("numPartitions", "2")
+      .table("h2.test.employee")
+      .groupBy(when(($"SALARY" > 8000).and($"SALARY" < 10000), $"SALARY").otherwise(0).as("key"))
+      .agg(Map.empty[String, String])
+    checkAggregateRemoved(df4, false)
+    checkPushedInfo(df4,
+      """
+        |PushedAggregates: [],
+        |PushedFilters: [],
+        |PushedGroupByExpressions:
+        |[CASE WHEN (SALARY > 8000.00) AND (SALARY < 10000.00) THEN SALARY ELSE 0.00 END],
+        |""".stripMargin.replaceAll("\n", " "))
+    checkAnswer(df4, Seq(Row(0), Row(9000)))
+  }
+
   test("scan with aggregate push-down: COUNT(col)") {
     val df = sql("select COUNT(DEPT) FROM h2.test.employee")
     checkAggregateRemoved(df)