[tiering] Tiering Job Performance: Read-Write Pipeline Optimization

### Search before asking

- [x] I searched in the [issues](https://github.com/apache/fluss/issues) and found nothing similar.

### Motivation

# Problem Description
When running a Tiering job with high write throughput, the data synchronization cannot keep up with the write speed. The root cause analysis reveals two main issues:

1. Parallelism is bounded by bucket count - Tiering job parallelism is 1:1 mapped to bucket count, limiting scalability
2. Read and write operations cannot be pipelined - Reading from Fluss and writing to Paimon are executed sequentially, resulting in low CPU utilization

# Root Cause Analysis

1. Split Granularity Equals Bucket Granularity: Each split covers exactly one bucket, which limits the maximum parallelism.
2. Sequential Read-Write Pattern: The current implementation reads from Fluss and writes to Paimon synchronously.

### Solution

_No response_

### Anything else?

_No response_

### Willingness to contribute

- [ ] I'm willing to submit a PR!

Start by locating the Tiering job implementation, especially the split handling and the code that reads from Fluss and writes to Paimon. Trace how bucket-to-split mapping sets parallelism and where read and write operations are serialized. Done means the job can scale beyond bucket-count parallelism and improve read-write throughput without losing synchronization correctness.

Written by the indexing model from the issue text.