diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/UnwrapCastInBinaryComparison.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/UnwrapCastInBinaryComparison.scala
index 89f7c0f71b7ac..d0acfe036d443 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/UnwrapCastInBinaryComparison.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/UnwrapCastInBinaryComparison.scala
@@ -184,7 +184,7 @@ object UnwrapCastInBinaryComparison extends Rule[LogicalPlan] {
     } else {
       // This means `value` is within range `(min, max)`. Optimize this by moving the cast to the
       // literal side.
-      val lit = Cast(Literal(value), fromType)
+      val lit = Literal(Cast(Literal(value), fromType).eval(), fromType)
       exp match {
         case GreaterThan(_, _) => GreaterThan(fromExp, lit)
         case GreaterThanOrEqual(_, _) => GreaterThanOrEqual(fromExp, lit)
@@ -202,9 +202,12 @@ object UnwrapCastInBinaryComparison extends Rule[LogicalPlan] {
    * i.e., the conversion is injective. Note this only handles the case when both sides are of
    * integral type.
    */
-  private def canImplicitlyCast(fromExp: Expression, toType: DataType,
+  private def canImplicitlyCast(
+      fromExp: Expression,
+      toType: DataType,
       literalType: DataType): Boolean = {
     toType.sameType(literalType) &&
+      !fromExp.foldable &&
       fromExp.dataType.isInstanceOf[IntegralType] &&
       toType.isInstanceOf[IntegralType] &&
       Cast.canUpCast(fromExp.dataType, toType)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnwrapCastInBinaryComparisonSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnwrapCastInBinaryComparisonSuite.scala
index 387964088b808..373c1febd2488 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnwrapCastInBinaryComparisonSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnwrapCastInBinaryComparisonSuite.scala
@@ -26,14 +26,14 @@ import org.apache.spark.sql.catalyst.optimizer.UnwrapCastInBinaryComparison._
 import org.apache.spark.sql.catalyst.plans.PlanTest
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules.RuleExecutor
-import org.apache.spark.sql.types.{BooleanType, ByteType, DoubleType, IntegerType}
+import org.apache.spark.sql.types._
 
 class UnwrapCastInBinaryComparisonSuite extends PlanTest with ExpressionEvalHelper {
 
   object Optimize extends RuleExecutor[LogicalPlan] {
     val batches: List[Batch] =
       Batch("Unwrap casts in binary comparison", FixedPoint(10),
-        NullPropagation, ConstantFolding, UnwrapCastInBinaryComparison) :: Nil
+        NullPropagation, UnwrapCastInBinaryComparison) :: Nil
   }
 
   val testRelation: LocalRelation = LocalRelation('a.short, 'b.float)
@@ -97,7 +97,7 @@ class UnwrapCastInBinaryComparisonSuite extends PlanTest with ExpressionEvalHelp
     assertEquivalent(Literal(v.toInt) >= castInt(f), trueIfNotNull(f))
     assertEquivalent(Literal(v.toInt) > castInt(f), f =!= v)
 
-    assertEquivalent(Literal(30) <= castInt(f), Literal(30.toShort) <= f)
+    assertEquivalent(Literal(30) <= castInt(f), Literal(30.toShort, ShortType) <= f)
   }
 
   test("unwrap cast should have no effect when input is not integral type") {
@@ -119,10 +119,12 @@ class UnwrapCastInBinaryComparisonSuite extends PlanTest with ExpressionEvalHelp
     )
   }
 
-  test("unwrap cast should skip when expression is non-deterministic") {
+  test("unwrap cast should skip when expression is non-deterministic or foldable") {
     Seq(positiveInt, negativeInt).foreach (v => {
       val e = Cast(First(f, ignoreNulls = true), IntegerType) <=> v
       assertEquivalent(e, e, evaluate = false)
+      val e2 = Cast(Literal(30.toShort), IntegerType) >= v
+      assertEquivalent(e2, e2, evaluate = false)
     })
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/FileBasedDataSourceSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/FileBasedDataSourceSuite.scala
index 8d6d93d13d143..f72e3347510f9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/FileBasedDataSourceSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/FileBasedDataSourceSuite.scala
@@ -32,14 +32,13 @@ import org.apache.spark.scheduler.{SparkListener, SparkListenerTaskEnd}
 import org.apache.spark.sql.TestingUDT.{IntervalUDT, NullData, NullUDT}
 import org.apache.spark.sql.catalyst.expressions.AttributeReference
 import org.apache.spark.sql.catalyst.expressions.IntegralLiteralTestUtils.{negativeInt, positiveInt}
-import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.catalyst.plans.logical.Filter
 import org.apache.spark.sql.execution.SimpleMode
 import org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanHelper
 import org.apache.spark.sql.execution.datasources.FilePartition
-import org.apache.spark.sql.execution.datasources.v2.{BatchScanExec, DataSourceV2ScanRelation, FileScan}
+import org.apache.spark.sql.execution.datasources.v2.{BatchScanExec, FileScan}
 import org.apache.spark.sql.execution.datasources.v2.orc.OrcScan
-import org.apache.spark.sql.execution.datasources.v2.parquet.{ParquetScan, ParquetTable}
+import org.apache.spark.sql.execution.datasources.v2.parquet.ParquetScan
 import org.apache.spark.sql.execution.joins.{BroadcastHashJoinExec, SortMergeJoinExec}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.internal.SQLConf