Spark 4.1: Support Iceberg time type

### Feature Request / Improvement

## Summary

Spark 4.1 introduced a native `TimeType` (SPARK-51162). Iceberg's `time` type can now be mapped to it, but the Spark module still rejects it: reading or writing a `time` column throws `UnsupportedOperationException: Spark does not support time fields` from `org.apache.iceberg.spark.TypeToSparkType`.

This revisits #9006, which was closed in 2019 — at the time Spark had no time type, so the suggested workaround was a timestamp pinned to 1970-01-01. That constraint no longer applies in Spark 4.1.

## Motivation / use case

Time-of-day values (e.g. MySQL `TIME` columns ingested via CDC) currently cannot land in Iceberg `time` columns when the query engine is Spark — they have to be cast to string as a workaround. With Spark 4.1's `TimeType`, a proper round-trip is now possible.

## Proposed scope
Add `time` support to the **Spark 4.1** module (`spark/v4.1`) only — `TimeType` does not exist in Spark 3.5 / 4.0.

- Type mapping: Iceberg `time` ⇄ Spark `TimeType` (microsecond precision).
- Value conversion: Iceberg stores time as **microseconds**-from-midnight, Spark 4.1 stores **nanoseconds** (SPARK-52460), so values are converted (×1000 on read, ÷1000 on write).
- Row-based reads/writes for Parquet, ORC, and Avro.

## Open question — vectorized reads

I'd like feedback on deferring vectorized reads. Spark 4.1's `ColumnarBatch` cannot expose `TimeType` values yet (`ColumnarBatchRow#get` throws `Datatype not supported TimeType(6)`), and the Arrow-based accessor lives in the shared `arrow` module (changing it would affect Flink and other engines). My current approach falls back to row-based reads when a `time` column is projected, leaving vectorized support as a follow-up. Does this seem like the right initial scope?

I have a working implementation (type mapping + value conversion + tests across Parquet/ORC/Avro) and will open a PR once there's agreement on the approach.

### Query engine

Spark

### Willingness to contribute

- [x] I can contribute this improvement/feature independently
- [ ] I would be willing to contribute this improvement/feature with guidance from the Iceberg community
- [ ] I cannot contribute this improvement/feature at this time

Begin with the spark/v4.1 module and org.apache.iceberg.spark.TypeToSparkType, then review the row-based Parquet, ORC, and Avro paths mentioned in the issue. Verify Spark 4.1 TimeType mapping and microsecond/nanosecond conversion, with round-trip coverage for all three formats and the intended row-based fallback for projected time columns.

Written by the indexing model from the issue text.