[SPARK-32220][SQL] SHUFFLE_REPLICATE_NL Hint should not change Non-Cartesian Product join result

### What changes were proposed in this pull request?
In current Join Hint strategies, if we use SHUFFLE_REPLICATE_NL hint, it will directly convert join to Cartesian Product Join and loss join condition making result not correct.

For Example:
```
spark-sql> select * from test4 order by a asc;
1 2
Time taken: 1.063 seconds, Fetched 4 row(s)20/07/08 14:11:25 INFO SparkSQLCLIDriver: Time taken: 1.063 seconds, Fetched 4 row(s)
spark-sql>select * from test5 order by a asc
1 2
2 2
Time taken: 1.18 seconds, Fetched 24 row(s)20/07/08 14:13:59 INFO SparkSQLCLIDriver: Time taken: 1.18 seconds, Fetched 24 row(s)spar
spark-sql>select /*+ shuffle_replicate_nl(test4) */ * from test4 join test5 where test4.a = test5.a order by test4.a asc ;
1 2 1 2
1 2 2 2
Time taken: 0.351 seconds, Fetched 2 row(s)
20/07/08 14:18:16 INFO SparkSQLCLIDriver: Time taken: 0.351 seconds, Fetched 2 row(s)
```

### Why are the changes needed?
Fix wrong data result

### Does this PR introduce _any_ user-facing change?
NO

### How was this patch tested?
Added UT

Closes #29035 from AngersZhuuuu/SPARK-32220.

Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>

Author: AngersZhuuuu

sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala

@@ -159,7 +159,7 @@ abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       //   4. Pick cartesian product if join type is inner like.
       //   5. Pick broadcast nested loop join as the final solution. It may OOM but we don't have
       //      other choice.
-      case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right, hint) =>
+      case p @ ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right, hint) =>
         def createBroadcastHashJoin(onlyLookingAtHint: Boolean) = {
           getBroadcastBuildSide(left, right, joinType, hint, onlyLookingAtHint, conf).map {
             buildSide =>
@@ -199,7 +199,7 @@ abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
 
         def createCartesianProduct() = {
           if (joinType.isInstanceOf[InnerLike]) {
-            Some(Seq(joins.CartesianProductExec(planLater(left), planLater(right), condition)))
+            Some(Seq(joins.CartesianProductExec(planLater(left), planLater(right), p.condition)))
           } else {
             None
           }

sql/core/src/test/scala/org/apache/spark/sql/JoinHintSuite.scala

@@ -570,4 +570,31 @@ class JoinHintSuite extends PlanTest with SharedSparkSession with AdaptiveSparkP
       assert(joinHints == expectedHints)
     }
   }
+
+  test("SPARK-32220: Non Cartesian Product Join Result Correct with SHUFFLE_REPLICATE_NL hint") {
+    withTempView("t1", "t2") {
+      Seq((1, "4"), (2, "2")).toDF("key", "value").createTempView("t1")
+      Seq((1, "1"), (2, "12.3"), (2, "123")).toDF("key", "value").createTempView("t2")
+      val df1 = sql("SELECT /*+ shuffle_replicate_nl(t1) */ * from t1 join t2 ON t1.key = t2.key")
+      val df2 = sql("SELECT * from t1 join t2 ON t1.key = t2.key")
+      assert(df1.collect().size == df2.collect().size)
+
+      val df3 = sql("SELECT /*+ shuffle_replicate_nl(t1) */ * from t1 join t2")
+      val df4 = sql("SELECT * from t1 join t2")
+      assert(df3.collect().size == df4.collect().size)
+
+      val df5 = sql("SELECT /*+ shuffle_replicate_nl(t1) */ * from t1 join t2 ON t1.key < t2.key")
+      val df6 = sql("SELECT * from t1 join t2 ON t1.key < t2.key")
+      assert(df5.collect().size == df6.collect().size)
+
+      val df7 = sql("SELECT /*+ shuffle_replicate_nl(t1) */ * from t1 join t2 ON t1.key < 2")
+      val df8 = sql("SELECT * from t1 join t2 ON t1.key < 2")
+      assert(df7.collect().size == df8.collect().size)
+
+
+      val df9 = sql("SELECT /*+ shuffle_replicate_nl(t1) */ * from t1 join t2 ON t2.key < 2")
+      val df10 = sql("SELECT * from t1 join t2 ON t2.key < 2")
+      assert(df9.collect().size == df10.collect().size)
+    }
+  }
 }