[FLINK-35180][python] Fix embedded (thread-mode) type converters

[polsinello]

What is the purpose of the change

In embedded (thread) mode, Pemja's JNI bridge auto-converts Python datetime/date/time objects to java.sql.Timestamp/Date/Time, but Flink's ExternalSerializer and DataFormatConverters.RowConverter expect the modern java.time.* bridge classes (LocalDateTime, LocalDate, LocalTime, Instant). This mismatch causes ClassCastException at serialization boundaries for any UDF that returns or receives temporal types. Process mode is unaffected because its Beam-based runnerOutputTypeSerializer resolves to the correct java.time.* serializers via LegacyTypeInfoDataTypeConverter.

This patch fixes several related type-conversion bugs in the embedded Python execution path so that all Flink-supported types work correctly in thread mode.

Brief change log

• Add bridge-aware DataConverter implementations for all temporal types (TIMESTAMP, DATE, TIME, TIMESTAMP_LTZ) on both the Table API (PythonTypeUtils in flink-table-runtime) and DataStream (PythonTypeUtils in flink-streaming-java) paths
• Replace IdentityDataConverter for temporal types where it silently passed java.sql.* objects through without conversion
• Fix ROW/TUPLE buffer reuse in ArrayDataConverter that caused silent data corruption in ARRAY<ROW<...>> results
• Widen numeric DataConverter generics from Long/Double to Number to avoid bridge-method ClassCastException when Pemja returns Integer/Float for small values
• Cache the original Java ExternalTypeInfo in the Python ExternalTypeInfo wrapper to prevent lossy DECIMAL precision round-trips through legacy TypeInfo conversion

Verifying this change

This change is already covered by existing tests — the existing PyFlink Table API and DataStream test suites exercise all affected type paths.

Does this pull request potentially affect one of the following parts:

• Dependencies (does it add or upgrade a dependency): no
• The public API, i.e., is any changed class annotated with @Public(Evolving): no
• The serializers: no (fixes type conversion before values reach the serializer)
• The runtime per-record code paths (performance sensitive): yes — the embedded Python UDF execution path now performs explicit type conversions for temporal/numeric types instead of relying on identity pass-through. The overhead is minimal (one java.time.* factory call per value).
• Anything that affects deployment or recovery: JobManager (and its components), Checkpointing, Kubernetes/Yarn, ZooKeeper: no
• The S3 file system connector: no

Documentation

• Does this pull request introduce a new feature? no
• If yes, how is the feature documented? not applicable

[flinkbot]

CI report:

• 4023fc6 Azure: FAILURE

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