[Bug] two fixed bucket pk table join to fixed bucket table cause low performance

### Search before asking

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

### Paimon version

My two paimon tables are both primary key tables with a fixed bucket size of 16. I'm using Spark SQL to execute a join statement on these two tables. One table has 80 million records, and the other has 150 million records. Using my SQL left join, writing to the non-primary key table (without a bucket key) is the fastest. However, if I write to a table with a fixed bucket key or dynamic bucketing, it becomes more than twice as slow. How can I eliminate the requirement for the primary key table to use fixed or dynamic bucketing? Otherwise, it severely impacts computational performance.

### Compute Engine

spark 3.5.2

### Minimal reproduce step

table A and B have same pk and same fixed bucket number,
table C same fixed bucket number,

insert into c
select * from A left join B on a.id=b.id and a.id1=b.id2

### What doesn't meet your expectations?

i can setting table c use no bucket mode to improve performance

### Anything else?

_No response_

### Are you willing to submit a PR?

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

Start with the Spark 3.5.2 reproduction using primary-key tables A and B, fixed bucket size 16, and an insert into table C, comparing fixed or dynamic bucketing with no bucket mode. Measure the left-join performance and trace the relevant Paimon table-writing and bucketing behavior; done means the reproducible slowdown is explained and a concrete performance improvement or supported limitation is documented.

Written by the indexing model from the issue text.