diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/CharVarcharUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/CharVarcharUtils.scala
index 75af56bdee828..4481063435847 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/CharVarcharUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/CharVarcharUtils.scala
@@ -215,7 +215,10 @@ object CharVarcharUtils extends Logging {
           Seq(Literal(f.name), processStringForCharVarchar(
             GetStructField(expr, i, Some(f.name)), f.dataType, charFuncName, varcharFuncName))
         })
-        if (expr.nullable) {
+        if (struct.valExprs.forall(_.isInstanceOf[GetStructField])) {
+          // No field needs char/varchar processing, just return the original expression.
+          expr
+        } else if (expr.nullable) {
           If(IsNull(expr), Literal(null, struct.dataType), struct)
         } else {
           struct
@@ -225,11 +228,18 @@ object CharVarcharUtils extends Logging {
         processStringForCharVarcharInArray(expr, et, containsNull, charFuncName, varcharFuncName)
 
       case MapType(kt, vt, valueContainsNull) =>
+        val keys = MapKeys(expr)
         val newKeys = processStringForCharVarcharInArray(
-          MapKeys(expr), kt, containsNull = false, charFuncName, varcharFuncName)
+          keys, kt, containsNull = false, charFuncName, varcharFuncName)
+        val values = MapValues(expr)
         val newValues = processStringForCharVarcharInArray(
-          MapValues(expr), vt, valueContainsNull, charFuncName, varcharFuncName)
-        MapFromArrays(newKeys, newValues)
+          values, vt, valueContainsNull, charFuncName, varcharFuncName)
+        if (newKeys.fastEquals(keys) && newValues.fastEquals(values)) {
+          // If map key/value does not need char/varchar processing, return the original expression.
+          expr
+        } else {
+          MapFromArrays(newKeys, newValues)
+        }
 
       case _ => expr
     }
@@ -242,10 +252,13 @@ object CharVarcharUtils extends Logging {
       charFuncName: Option[String],
       varcharFuncName: Option[String]): Expression = {
     val param = NamedLambdaVariable("x", replaceCharVarcharWithString(et), containsNull)
-    val func = LambdaFunction(
-      processStringForCharVarchar(param, et, charFuncName, varcharFuncName),
-      Seq(param))
-    ArrayTransform(arr, func)
+    val funcBody = processStringForCharVarchar(param, et, charFuncName, varcharFuncName)
+    if (funcBody.fastEquals(param)) {
+      // If array element does not need char/varchar processing, return the original expression.
+      arr
+    } else {
+      ArrayTransform(arr, LambdaFunction(funcBody, Seq(param)))
+    }
   }
 
   def addPaddingForScan(attr: Attribute): Expression = {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CharVarcharTestSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CharVarcharTestSuite.scala
index 27a630c169be0..95c2e5085d92e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/CharVarcharTestSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/CharVarcharTestSuite.scala
@@ -18,7 +18,9 @@
 package org.apache.spark.sql
 
 import org.apache.spark.{SparkConf, SparkException}
+import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.catalyst.parser.CatalystSqlParser
+import org.apache.spark.sql.catalyst.plans.logical.Project
 import org.apache.spark.sql.catalyst.util.CharVarcharUtils
 import org.apache.spark.sql.connector.SchemaRequiredDataSource
 import org.apache.spark.sql.connector.catalog.InMemoryPartitionTableCatalog
@@ -862,6 +864,26 @@ class FileSourceCharVarcharTestSuite extends CharVarcharTestSuite with SharedSpa
       }
     }
   }
+
+  test("SPARK-40697: read-side char padding should only be applied if necessary") {
+    withTable("t") {
+      sql(
+        s"""
+          |CREATE TABLE t (
+          |  c1 CHAR(5),
+          |  c2 STRUCT<i VARCHAR(5)>,
+          |  c3 ARRAY<VARCHAR(5)>,
+          |  c4 MAP<INT, VARCHAR(5)>
+          |) USING $format
+          |""".stripMargin)
+      spark.read.table("t").queryExecution.analyzed.foreach {
+        case Project(projectList, _) =>
+          assert(projectList.length == 4)
+          assert(projectList.drop(1).forall(_.isInstanceOf[Attribute]))
+        case _ =>
+      }
+    }
+  }
 }
 
 class DSV2CharVarcharTestSuite extends CharVarcharTestSuite