fix: reject string/binary read as numeric in native_datafusion scan

[andygrove]

Which issue does this PR close?

Closes #4088.

Rationale for this change

When the native_datafusion scan reads a Parquet BINARY (UTF8) column under a numeric read schema, the existing schema adapter creates a Spark Cast with is_adapting_schema=true. In that mode Cast delegates to DataFusion's cast, which parses the bytes (returning null on non-numeric strings, or in some paths reinterpreting the raw bytes). Spark's vectorized reader rejects this kind of mismatch with SchemaColumnConvertNotSupportedException on every supported version, and native_iceberg_compat already does the same via TypeUtil.checkParquetType. The native scan should match.

What changes are included in this PR?

native/core/src/parquet/schema_adapter.rs: in replace_with_spark_cast, add a guard before the existing branches that returns DataFusionError::Plan when the source type is Utf8, LargeUtf8, Binary, or LargeBinary and the target type is any integer (Int8/Int16/Int32/Int64/UInt*) or floating-point type (Float32/Float64). The rule mirrors TypeUtil.checkParquetType's BINARY case (lines 208-221), which only allows reading BINARY as StringType, BinaryType, or a binary-encoded decimal.

The check is intentionally narrow: it only fires for string/binary -> numeric mismatches and leaves every other type path unchanged.

How are these changes tested?

Added a focused test to ParquetReadSuite: native_datafusion rejects string read as numeric. It writes string data, reads it under c int, forces spark.comet.scan.impl=native_datafusion and spark.sql.sources.useV1SourceList=parquet, and asserts that collect() raises SparkException. Verified against ParquetReadV1Suite (44 succeeded, no regressions; 1 pre-existing test ignored).

The behavior is also covered by the per-impl matrix added in #4087 (string read as int: native_datafusion), whose assertion will need flipping from "succeeds with garbage" to "throws" once that PR merges.