[SPARK-30671][SQL] emptyDataFrame should use a LocalRelation

### What changes were proposed in this pull request?
This PR makes `SparkSession.emptyDataFrame` use an empty local relation instead of an empty RDD. This allows to optimizer to recognize this as an empty relation, and creates the opportunity to do some more aggressive optimizations.

### Why are the changes needed?
It allows us to optimize empty dataframes better.

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

### How was this patch tested?
Added a test case to `DataFrameSuite`.

Closes #27400 from hvanhovell/SPARK-30671.

Authored-by: herman <herman@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>

sql/core/src/main/scala/org/apache/spark/sql/SparkSession.scala

@@ -274,9 +274,7 @@ class SparkSession private(
    * @since 2.0.0
    */
   @transient
-  lazy val emptyDataFrame: DataFrame = {
-    createDataFrame(sparkContext.emptyRDD[Row].setName("empty"), StructType(Nil))
-  }
+  lazy val emptyDataFrame: DataFrame = Dataset.ofRows(self, LocalRelation())
 
   /**
    * Creates a new [[Dataset]] of type T containing zero elements.

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

@@ -33,7 +33,7 @@ import org.apache.spark.sql.catalyst.TableIdentifier
 import org.apache.spark.sql.catalyst.encoders.RowEncoder
 import org.apache.spark.sql.catalyst.expressions.Uuid
 import org.apache.spark.sql.catalyst.optimizer.ConvertToLocalRelation
-import org.apache.spark.sql.catalyst.plans.logical.{OneRowRelation, Union}
+import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, OneRowRelation, Union}
 import org.apache.spark.sql.execution.{FilterExec, QueryExecution, WholeStageCodegenExec}
 import org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanHelper
 import org.apache.spark.sql.execution.aggregate.HashAggregateExec
@@ -2287,6 +2287,17 @@ class DataFrameSuite extends QueryTest
     }
     assert(err.getMessage.contains("cannot resolve '`d`'"))
   }
+
+  test("emptyDataFrame should be foldable") {
+    val emptyDf = spark.emptyDataFrame.withColumn("id", lit(1L))
+    val joined = spark.range(10).join(emptyDf, "id")
+    joined.queryExecution.optimizedPlan match {
+      case LocalRelation(Seq(id), Nil, _) =>
+        assert(id.name == "id")
+      case _ =>
+        fail("emptyDataFrame should be foldable")
+    }
+  }
 }
 
 case class GroupByKey(a: Int, b: Int)