[SPARK-42302][SQL] Assign name to _LEGACY_ERROR_TEMP_2135

### What changes were proposed in this pull request?

This PR proposes to assign name to _LEGACY_ERROR_TEMP_2135, "FAILED_PARSE_EMPTY_STRING".

### Why are the changes needed?

We should assign proper name to _LEGACY_ERROR_TEMP_*

### Does this PR introduce _any_ user-facing change?

No

### How was this patch tested?

`./build/sbt "sql/testOnly org.apache.spark.sql.SQLQueryTestSuite*"`

Closes #39872 from itholic/LEGACY_2135.

Lead-authored-by: itholic <[email protected]>
Co-authored-by: Haejoon Lee <[email protected]>
Signed-off-by: Max Gekk <[email protected]>

Author: itholic

core/src/main/resources/error/error-classes.json

@@ -506,6 +506,12 @@
     ],
     "sqlState" : "22003"
   },
+  "EMPTY_JSON_FIELD_VALUE" : {
+    "message" : [
+      "Failed to parse an empty string for data type <dataType>."
+    ],
+    "sqlState" : "42604"
+  },
   "ENCODER_NOT_FOUND" : {
     "message" : [
       "Not found an encoder of the type <typeName> to Spark SQL internal representation. Consider to change the input type to one of supported at https://spark.apache.org/docs/latest/sql-ref-datatypes.html."
@@ -4237,11 +4243,6 @@
       "Cannot parse field value <value> for pattern <pattern> as target spark data type [<dataType>]."
     ]
   },
-  "_LEGACY_ERROR_TEMP_2135" : {
-    "message" : [
-      "Failed to parse an empty string for data type <dataType>."
-    ]
-  },
   "_LEGACY_ERROR_TEMP_2138" : {
     "message" : [
       "Cannot have circular references in bean class, but got the circular reference of class <clazz>."

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/json/JacksonParser.scala

@@ -420,12 +420,12 @@ class JacksonParser(
     case VALUE_STRING if parser.getTextLength < 1 && allowEmptyString =>
       dataType match {
         case FloatType | DoubleType | TimestampType | DateType =>
-          throw QueryExecutionErrors.failToParseEmptyStringForDataTypeError(dataType)
+          throw QueryExecutionErrors.emptyJsonFieldValueError(dataType)
         case _ => null
       }
 
     case VALUE_STRING if parser.getTextLength < 1 =>
-      throw QueryExecutionErrors.failToParseEmptyStringForDataTypeError(dataType)
+      throw QueryExecutionErrors.emptyJsonFieldValueError(dataType)
 
     case token =>
       // We cannot parse this token based on the given data type. So, we throw a

sql/catalyst/src/main/scala/org/apache/spark/sql/errors/QueryExecutionErrors.scala

@@ -1431,11 +1431,10 @@ private[sql] object QueryExecutionErrors extends QueryErrorsBase {
         "dataType" -> dataType.toString()))
   }
 
-  def failToParseEmptyStringForDataTypeError(dataType: DataType): SparkRuntimeException = {
+  def emptyJsonFieldValueError(dataType: DataType): SparkRuntimeException = {
     new SparkRuntimeException(
-      errorClass = "_LEGACY_ERROR_TEMP_2135",
-      messageParameters = Map(
-        "dataType" -> dataType.catalogString))
+      errorClass = "EMPTY_JSON_FIELD_VALUE",
+      messageParameters = Map("dataType" -> toSQLType(dataType)))
   }
 
   def cannotParseJSONFieldError(parser: JsonParser, jsonType: JsonToken, dataType: DataType)

sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala

@@ -35,6 +35,7 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{functions => F, _}
 import org.apache.spark.sql.catalyst.json._
 import org.apache.spark.sql.catalyst.util.{DateTimeTestUtils, DateTimeUtils}
+import org.apache.spark.sql.catalyst.util.TypeUtils.toSQLType
 import org.apache.spark.sql.execution.ExternalRDD
 import org.apache.spark.sql.execution.datasources.{CommonFileDataSourceSuite, DataSource, InMemoryFileIndex, NoopCache}
 import org.apache.spark.sql.execution.datasources.v2.json.JsonScanBuilder
@@ -2608,11 +2609,12 @@ abstract class JsonSuite
   private def failedOnEmptyString(dataType: DataType): Unit = {
     val df = spark.read.schema(s"a ${dataType.catalogString}")
       .option("mode", "FAILFAST").json(Seq("""{"a":""}""").toDS)
-    val errMessage = intercept[SparkException] {
-      df.collect()
-    }.getMessage
-    assert(errMessage.contains(
-      s"Failed to parse an empty string for data type ${dataType.catalogString}"))
+    val e = intercept[SparkException] {df.collect()}
+    checkError(
+      exception = e.getCause.getCause.getCause.asInstanceOf[SparkRuntimeException],
+      errorClass = "EMPTY_JSON_FIELD_VALUE",
+      parameters = Map("dataType" -> toSQLType(dataType))
+    )
   }
 
   private def emptyString(dataType: DataType, expected: Any): Unit = {