Add a task-manifest output committer for Azure and GCS

Add a task-manifest output committer for Azure and GCS

The S3A committers are very popular in Spark on S3, as they are both correct and fast.

The classic FileOutputCommitter v1 and v2 algorithms are all that is available for Azure ABFS and Google GCS, and they have limitations.

The v2 algorithm isn't safe in the presence of failed task attempt commits, so we
recommend the v1 algorithm for Azure. But that is slow because it sequentially lists
then renames files and directories, one-by-one. The latencies of list
and rename make things slow.

Google GCS lacks the atomic directory rename required for v1 correctness;
v2 can be used (which doesn't have the job commit performance limitations),
but it's not safe.

Proposed

• Add a new FileOutputFormat committer which uses an intermediate manifest to
  pass the list of files created by a TA to the job committer.
• Job committer to parallelise reading these task manifests and submit all the
  rename operations into a pool of worker threads. (also: mkdir, directory deletions on cleanup)
• Use the committer plugin mechanism added for s3a to make this the default committer for ABFS
  (i.e. no need to make any changes to FileOutputCommitter)
• Add lots of IOStatistics instrumentation + logging of operations in the JobCommit
  for visibility of where delays are occurring.
• Reuse the S3A committer _SUCCESS JSON structure to publish IOStats & other data
  for testing/support.

This committer will be faster than the V1 algorithm because of the parallelisation, and
because a manifest written by create-and-rename will be exclusive to a single task
attempt, delivers the isolation which the v2 committer lacks.

This is not an attempt to do an iceberg/hudi/delta-lake style manifest-only format
for describing the contents of a table; the final output is still a directory tree
which must be scanned during query planning.
As such the format is still suboptimal for cloud storage -but at least we will have
faster job execution during the commit phases.

Note: this will also work on HDFS, where again, it should be faster than
the v1 committer. However the target is very much Spark with ABFS and GCS; no plans to worry about MR as that simplifies the challenge of dealing with job restart (i.e. you don't have to)