UnknownException: (org.apache.spark.SparkSQLException) [UNRECOGNIZED_SQL_TYPE] Unrecognized SQL type - name: UInt64, id: OTHER. SQLSTATE: 42704

## Description
The Spark JDBC V2 data source fails to recognize the UInt64 data type from ClickHouse, resulting in a SparkSQLException. This prevents reading tables that contain UInt64 columns. The issue appears in clickhouse-jdbc driver versions newer than 0.8.2.

[(https://github.com/ClickHouse/clickhouse-java/issues/1042)] - it looks like a similar issue to this one, but it occurs for the UInt* types.

### Steps to reproduce
Set up an Apache Spark 4.0 cluster.
In a ClickHouse database (version 25.4.3.22), create a table containing a UInt64 column.
Attempt to read from this table using Apache Spark's JDBC data source with clickhouse-jdbc driver version 0.9.0.

### Error Log or Exception StackTrace
```
UnknownException: (org.apache.spark.SparkSQLException) [UNRECOGNIZED_SQL_TYPE] Unrecognized SQL type - name: UInt64, id: OTHER. SQLSTATE: 42704

JVM stacktrace:
org.apache.spark.SparkSQLException
at org.apache.spark.sql.errors.QueryExecutionErrors$.unrecognizedSqlTypeError(QueryExecutionErrors.scala:977)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.getCatalystType(JdbcUtils.scala:241)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$getSchema$1(JdbcUtils.scala:325)
at scala.Option.getOrElse(Option.scala:201)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.getSchema(JdbcUtils.scala:325)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$.$anonfun$getQueryOutputSchema$3(JDBCRDD.scala:72)
at scala.util.Using$.resource(Using.scala:296)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$.$anonfun$getQueryOutputSchema$2(JDBCRDD.scala:70)
at scala.util.Using$.resource(Using.scala:296)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$.$anonfun$getQueryOutputSchema$1(JDBCRDD.scala:68)
at scala.util.Using$.resource(Using.scala:296)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$.getQueryOutputSchema(JDBCRDD.scala:67)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$.resolveTable(JDBCRDD.scala:62)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRelation$.getSchema(JDBCRelation.scala:243)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:38)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:361)
at org.apache.spark.sql.catalyst.analysis.ResolveDataSource.org$apache$spark$sql$catalyst$analysis$ResolveDataSource$$loadV1BatchSource(ResolveDataSource.scala:143)
at org.apache.spark.sql.catalyst.analysis.ResolveDataSource$$anonfun$apply$1.$anonfun$applyOrElse$2(ResolveDataSource.scala:61)
at scala.Option.getOrElse(Option.scala:201)
at org.apache.spark.sql.catalyst.analysis.ResolveDataSource$$anonfun$apply$1.applyOrElse(ResolveDataSource.scala:61)
at org.apache.spark.sql.catalyst.analysis.ResolveDataSource$$anonfun$apply$1.applyOrElse(ResolveDataSource.scala:45)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$3(AnalysisHelper.scala:139)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(origin.scala:86)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$1(AnalysisHelper.scala:139)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:416)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning(AnalysisHelper.scala:135)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning$(AnalysisHelper.scala:131)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsUpWithPruning(LogicalPlan.scala:37)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUp(AnalysisHelper.scala:112)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUp$(AnalysisHelper.scala:111)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsUp(LogicalPlan.scala:37)
at org.apache.spark.sql.catalyst.analysis.ResolveDataSource.apply(ResolveDataSource.scala:45)
at org.apache.spark.sql.catalyst.analysis.ResolveDataSource.apply(ResolveDataSource.scala:43)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$2(RuleExecutor.scala:242)
at scala.collection.LinearSeqOps.foldLeft(LinearSeq.scala:183)
at scala.collection.LinearSeqOps.foldLeft$(LinearSeq.scala:179)
at scala.collection.immutable.List.foldLeft(List.scala:79)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:239)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:231)
at scala.collection.immutable.List.foreach(List.scala:334)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:231)
at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:290)
at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$execute$1(Analyzer.scala:286)
at org.apache.spark.sql.catalyst.analysis.AnalysisContext$.withNewAnalysisContext(Analyzer.scala:234)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:286)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:249)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:201)
at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:89)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:201)
at org.apache.spark.sql.catalyst.analysis.resolver.HybridAnalyzer.resolveInFixedPoint(HybridAnalyzer.scala:190)
at org.apache.spark.sql.catalyst.analysis.resolver.HybridAnalyzer.$anonfun$apply$1(HybridAnalyzer.scala:76)
at org.apache.spark.sql.catalyst.analysis.resolver.HybridAnalyzer.withTrackedAnalyzerBridgeState(HybridAnalyzer.scala:111)
at org.apache.spark.sql.catalyst.analysis.resolver.HybridAnalyzer.apply(HybridAnalyzer.scala:71)
at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$executeAndCheck$1(Analyzer.scala:280)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:423)
at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:280)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$lazyAnalyzed$2(QueryExecution.scala:110)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:148)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$2(QueryExecution.scala:278)
at org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:654)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$1(QueryExecution.scala:278)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:804)
at org.apache.spark.sql.execution.QueryExecution.executePhase(QueryExecution.scala:277)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$lazyAnalyzed$1(QueryExecution.scala:110)
at scala.util.Try$.apply(Try.scala:217)
at org.apache.spark.util.Utils$.doTryWithCallerStacktrace(Utils.scala:1378)
at org.apache.spark.util.Utils$.getTryWithCallerStacktrace(Utils.scala:1439)
at org.apache.spark.util.LazyTry.get(LazyTry.scala:58)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:121)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:80)
at org.apache.spark.sql.classic.Dataset$.$anonfun$ofRows$1(Dataset.scala:115)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:804)
at org.apache.spark.sql.classic.Dataset$.ofRows(Dataset.scala:113)
at org.apache.spark.sql.classic.DataFrameReader.load(DataFrameReader.scala:109)
at org.apache.spark.sql.classic.DataFrameReader.load(DataFrameReader.scala:92)
at org.apache.spark.sql.connect.planner.SparkConnectPlanner.transformReadRel(SparkConnectPlanner.scala:1409)
at org.apache.spark.sql.connect.planner.SparkConnectPlanner.$anonfun$transformRelation$1(SparkConnectPlanner.scala:152)
at org.apache.spark.sql.connect.service.SessionHolder.$anonfun$usePlanCache$3(SessionHolder.scala:477)
at scala.Option.getOrElse(Option.scala:201)
at org.apache.spark.sql.connect.service.SessionHolder.usePlanCache(SessionHolder.scala:476)
at org.apache.spark.sql.connect.planner.SparkConnectPlanner.transformRelation(SparkConnectPlanner.scala:147)
at org.apache.spark.sql.connect.planner.SparkConnectPlanner.transformRelation(SparkConnectPlanner.scala:133)
at org.apache.spark.sql.connect.planner.SparkConnectPlanner.transformShowString(SparkConnectPlanner.scala:306)
at org.apache.spark.sql.connect.planner.SparkConnectPlanner.$anonfun$transformRelation$1(SparkConnectPlanner.scala:150)
at org.apache.spark.sql.connect.service.SessionHolder.$anonfun$usePlanCache$3(SessionHolder.scala:477)
at scala.Option.getOrElse(Option.scala:201)
at org.apache.spark.sql.connect.service.SessionHolder.usePlanCache(SessionHolder.scala:476)
at org.apache.spark.sql.connect.planner.SparkConnectPlanner.transformRelation(SparkConnectPlanner.scala:147)
at org.apache.spark.sql.connect.execution.SparkConnectPlanExecution.handlePlan(SparkConnectPlanExecution.scala:74)
at org.apache.spark.sql.connect.execution.ExecuteThreadRunner.handlePlan(ExecuteThreadRunner.scala:314)
at org.apache.spark.sql.connect.execution.ExecuteThreadRunner.$anonfun$executeInternal$1(ExecuteThreadRunner.scala:225)
at org.apache.spark.sql.connect.execution.ExecuteThreadRunner.$anonfun$executeInternal$1$adapted(ExecuteThreadRunner.scala:196)
at org.apache.spark.sql.connect.service.SessionHolder.$anonfun$withSession$2(SessionHolder.scala:341)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:804)
at org.apache.spark.sql.connect.service.SessionHolder.$anonfun$withSession$1(SessionHolder.scala:341)
at org.apache.spark.JobArtifactSet$.withActiveJobArtifactState(JobArtifactSet.scala:94)
at org.apache.spark.sql.artifact.ArtifactManager.$anonfun$withResources$1(ArtifactManager.scala:112)
at org.apache.spark.util.Utils$.withContextClassLoader(Utils.scala:186)
at org.apache.spark.sql.artifact.ArtifactManager.withClassLoaderIfNeeded(ArtifactManager.scala:102)
at org.apache.spark.sql.artifact.ArtifactManager.withResources(ArtifactManager.scala:111)
at org.apache.spark.sql.connect.service.SessionHolder.withSession(SessionHolder.scala:340)
at org.apache.spark.sql.connect.execution.ExecuteThreadRunner.executeInternal(ExecuteThreadRunner.scala:196)
at org.apache.spark.sql.connect.execution.ExecuteThreadRunner.org$apache$spark$sql$connect$execution$ExecuteThreadRunner$$execute(ExecuteThreadRunner.scala:125)
at org.apache.spark.sql.connect.execution.ExecuteThreadRunner$ExecutionThread.run(ExecuteThreadRunner.scala:347)
```

### Expected Behaviour
Spark should successfully read the data from the ClickHouse table. The UInt64 ClickHouse type should be mapped to a compatible Spark SQL type, such as DecimalType(20, 0) or LongType if the values fit.

### Code Example

```python

ch_df = spark.read \
.format("jdbc") \
.option("url","jdbc:clickhouse://ip_address:port/spark_data") \
.option("dbtable","(SELECT * FROM spark_data.test_uint64_table) as source_tmp") \
.option("user","user") \
.option("password","password") \
.option("driver","com.clickhouse.jdbc.ClickHouseDriver") \
.load()
ch_df.show()

```

### Configuration

#### Client Configuration
```python

```

#### Environment
*[ ]
*Client version: clickhouse-jdbc-0.9.0.jar
*Language version: Apache Spark 4.0 (PySpark)
*OS: Ubuntu 22.04

#### ClickHouse Server
* ClickHouse Server version: 25.4.3.22
* ClickHouse Server non-default settings, if any:None
```
CREATE TABLE spark_data.test_uint64_table
(
`id` UInt64,
`event_name` String
)
ENGINE = MergeTree()
ORDER BY id
```

```
INSERT INTO spark_data.test_uint64_table (id, event_name) VALUES (18446744073709551615, 'max_value_event');
INSERT INTO spark_data.test_uint64_table (id, event_name) VALUES (1, 'regular_event');
```

Bắt đầu bằng cách tái hiện lỗi với clickhouse-jdbc 0.9.0, Spark 4.0 và một bảng ClickHouse chứa UInt64, sau đó truy vết siêu dữ liệu kiểu của trình điều khiển JDBC được sử dụng trong quá trình Spark phân giải schema. Công việc hoàn tất khi Spark có thể tải bảng và UInt64 được cung cấp dưới dạng một kiểu Spark SQL tương thích, với coverage cho kiểu cột này.

Do mô hình lập chỉ mục viết ra từ nội dung của issue.