Upsertion memory usage grows exponentially as table size grows

### Apache Iceberg version

0.9.0

### Please describe the bug 🐞

Hello,

I am trying out the new upsert method on a table created as follows on the AWS Glue Catalog :

```python
catalog = load_catalog("glue", **{"type": "glue"})
schema = Schema(
NestedField(1, "dt_insert", StringType(), required=True),
NestedField(2, "controller_id", StringType(), required=True),
NestedField(3, "timestamp", StringType(), required=True),
NestedField(4, "data_type", StringType(), required=True),
NestedField(5, "parameter", StringType(), required=True),
NestedField(6, "value", StringType(), required=True),
NestedField(7, "data_source", StringType(), required=True),
NestedField(8, "unique_id", StringType(), required=True),
NestedField(9, "dt_import_utc", StringType(), required=True),
identifier_field_ids=[8], # 'unique_id' is the primary key
)
catalog.create_table(
identifier=.,
schema=schema,
partition_spec=PartitionSpec(
PartitionField(
source_id=9,
field_id=1000,
transform=IdentityTransform(),
name="dt_import_utc",
),
),
sort_order=SortOrder(
SortField(source_id=2), # controller_id
SortField(source_id=4), # data_type
SortField(source_id=3), # dt_insert
SortField(source_id=1), # timestamp
),
location="s3://bucket/prefix/catalog",
properties={
"write.format.default" : "parquet",
"write.target-file-size-bytes" : 134217728, # 128 MB
"write.metadata.delete-after-commit.enabled" : True,
"write.metadata.previous-versions-max" : 5
})
```

`unique_id` is a the merged string of columns 1, 2, 3, 4 and 5 with an underscore.

My upsertion is a simple pandas dataframe dumped into a dict and being transformed to pyarrow for upsertion (I know pyarrow accepts directly a pandas df but doing this for an internal reason that requires writing the df in a stage and read in another, so i am dumping as json for readability).

```python
iceberg_table = catalog.load_table(table_name)
table = pa.Table.from_pydict(to_upsert, schema=iceberg_table.schema().as_arrow())
iceberg_table.upsert(df=table, join_cols=["unique_id"])
```

With AWS Athena, a MERGE INTO statement takes about 3 seconds to run on the table and scans 1.11MB of data before completion.

```sql
MERGE INTO . target
USING . source
ON (target."unique_id" = source."unique_id")
WHEN MATCHED THEN
UPDATE SET "dt_insert" = source."dt_insert", "controller_id" = source."controller_id", "timestamp" = source."timestamp", "data_type" = source."data_type", "parameter" = source."parameter", "value" = source."value", "data_source" = source."data_source", "dt_import_utc" = source."dt_import_utc", "unique_id" = source."unique_id"
WHEN NOT MATCHED THEN
INSERT ("dt_insert", "controller_id", "timestamp", "data_type", "parameter", "value", "data_source", "dt_import_utc", "unique_id")
VALUES (source."dt_insert", source."controller_id", source."timestamp", source."data_type", source."parameter", source."value", source."data_source", source."dt_import_utc", source."unique_id")
```
(statement generated by using the `to_iceberg` from `awswrangler`)

Meanwhile when i try with pyiceberg upsert, it is using more than 10240MB. I am running on AWS lambda and it is causing an out of memory error.

I have no issue with the `append()` function, it completes fairly quickly but it seems that the upsert needs further optimization to be able to efficiently retrieve only relevant data.

Current table size is at 18.5GB for both Athena based statement and pyiceberg upsert function call.

### 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
- [x] I cannot contribute a fix for this bug at this time

Start at the PyIceberg table.upsert entry point and compare its behavior with append(), using the provided AWS Glue table schema and join_cols=["unique_id"] reproduction. Measure memory usage as the table grows and identify whether the upsert can limit data retrieval to relevant rows; done means the reproduction completes without the reported out-of-memory failure.

Written by the indexing model from the issue text.