Delete/Overwrite/Upsert with string key containing dot (.) hangs indefinitely

### Apache Iceberg version

None

### Please describe the bug 🐞

### Description

Any operation that rewrites data (delete/ overwrite/ upsert) is extremely slow or unresponsive, even on very small tables. Appending rows works as expected.

### Steps to Reproduce

```
from pyiceberg.schema import Schema
from pyiceberg.types import NestedField, StringType
import pyarrow as pa

# Create table
schema = Schema(
NestedField(field_id=1, name="load_id", field_type=StringType(), required=True),
NestedField(field_id=2, name="status", field_type=StringType(), required=True),
identifier_field_ids=[1],
)

catalog.create_table(identifier="test.test_load", schema=schema)

tbl = catalog.load_table("test.test_load")
df = pa.Table.from_pylist(
[
{"load_id": "123.123", "status": "started"},
{"load_id": "456.456", "status": "done"},
],
schema=tbl.schema().as_arrow(),
)
tbl.append(df)

# Delete with dot in string key → hangs indefinitely
tbl.delete(delete_filter="load_id == '123.123'")
```

### Observed Behavior

- append: completes quickly.
- delete with string containing dot (e.g., "123.123"): runs indefinitely or takes 10+ minutes without returning a response.
- overwrite and upsert with string containing dot: same behavior — operation never completes or is extremely slow.

### Environment

- PyIceberg version: `0.10.0`
- Catalog: `REST`
- Python version: `3.12`
- Storage: `AWS S3 tables`

### Willingness to contribute

- [ ] I can contribute a fix for this bug independently
- [ ] I would be willing to contribute a fix for this bug with guidance from the Iceberg community
- [ ] I cannot contribute a fix for this bug at this time

Start by running the provided Python reproduction against the PyIceberg table and trace the tbl.delete entry point with the filter load_id == '123.123'. Compare delete, overwrite, and upsert behavior with and without the dot; done means these operations complete promptly for string keys containing dots.

Written by the indexing model from the issue text.