[Feature] Spark Merge optimization : reading only key columns from target table

### Search before asking

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

### Motivation

We merge 1 million rows from source into target with 10 million rows:
```
MERGE INTO TableIcebergMOR target
USING TableIcebergMOR_1000000 source
ON target.id = source.id
WHEN MATCHED THEN
UPDATE SET *
WHEN NOT MATCHED
THEN INSERT *
```
In physical plan we see, that target table is full scaned and shuffled :

Same query in Apache Iceberg scans only needed columns, so scan and shuffle **is times faster**:

Is it possible add same optimization in Paimon?
### Solution

_No response_

### Anything else?

_No response_

### Are you willing to submit a PR?

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

The issue names no files, tests, or implementation entry points. Start by reproducing the shown Spark MERGE query and inspecting its physical plan, then trace how Paimon chooses target-table columns and shuffles them. Done means the target scan and shuffle read only columns required by the merge while preserving the query behavior.

Written by the indexing model from the issue text.