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

从 PyIceberg table.upsert 入口开始，使用提供的 AWS Glue 表 schema 和 join_cols=["unique_id"] 的复现来比较其与 append() 的行为。测量表不断增长时的内存使用情况，并确定 upsert 是否可以将数据检索限制为相关行；当复现能够在不出现所报告的内存不足错误的情况下完成时，即视为完成。

由索引模型根据 Issue 内容生成。