Add a native Iceberg write benchmark #5647

What is the problem the feature request solves?

The only performance numbers for native Iceberg writes are the ad hoc measurements in the #5361 description (2.9x unpartitioned, 5.5x partitioned fanout, from a harness in a personal fork). There is no benchmark in the repository, so a regression in the writer, in the shuffle feeding it, or in the JVM metrics rebuild would not be noticed, and the partitioned number was measured with write.distribution-mode=none rather than the default plan shape.

Describe the potential solution

Add a Spark benchmark (the org.apache.spark.sql.benchmark framework used by CometReadBenchmark and the shuffle benchmark) that measures INSERT INTO ... SELECT into an Iceberg table for:

• unpartitioned
• partitioned with the default hash distribution (clustered writer, native shuffle and sort on the partition transforms after Support Iceberg's system functions (bucket, truncate, years/months/days/hours) as native expressions #5635)
• partitioned with write.spark.fanout.enabled=true
• a copy-on-write DELETE (rewrite of affected files)

Each case runs Spark, Comet scan only, and Comet scan plus native write, so the writer's contribution is isolated the way the #5361 table did. Wire it into the benchmark targets in the Makefile and document how to run it. Follow-on: run it on the EC2 benchmark runner and keep results with the other benchmark files.

Keep the Spark benchmark framework's warmup and minimum-time floors in mind when sizing the data (see the shuffle benchmark work in #5388 for how that dominated wall clock).

Additional context

Part of the native Iceberg writes epic, #5649. Related: #5361, #3595 (fused sort plus write, which this benchmark would measure).