load_table consumes enormous amounts of memory on large metadata file

### Apache Iceberg version

0.11.0 (latest release)

### Please describe the bug 🐞

Apologies, this is a bit of a fuzzy one right now, but I thought reporting it anyway.

**Context:**
We're using Iceberg with AWS Glue and AWS S3 as storage. In S3 there are roughly speaking 3 kinds of files (metadata, manifests, and data files). The first one that is read when loading a table via `catalog.load_table()` is the metadata file. The metadata file contains information on all current* snapshots and schema versions of the table. py-iceberg seems to load these completely into memory.

**Issue:**
As we worked on the Iceberg table, there were a lot of snapshots created over time and with that a lot of schema versions. This led to the latest metadata file to be grow to ~10MB gzip compressed (or ~250MB uncompressed JSON). When we load this table via `catalog.load_table()` it consumes ~4GB of memory (total usage of the python process in memray). This is a lot - especially since we only need the latest snapshot and the respective schema version. (Which is probably true for most users I guess.)

**Semi-Workaround:**
One could try to expire some snapshots, e.g. via Sparks `expire_snapshots` procedure [https://iceberg.apache.org/docs/1.10.0/spark-procedures/#expire_snapshots], but it will not get rid of the old / unused schemas unless you set `clean_expired_metadata` as well (which is only supported since 1.10.x, so relatively new).

**(Preliminary) Root-Cause:**
I believe the issue is that we leverage Pydantic's `model_validate_json` in https://github.com/apache/iceberg-python/blob/44ce51a939ccbacf9c87ce6593ad43a752b0871b/pyiceberg/table/metadata.py#L663, which loads the whole JSON into memory and then we seem to keep the full `TableMetadata` object around.

**Suggestion:**
Would it make sense to parse the JSON not fully into memory and load the needed snapshots and schemas lazy / on demand? (Would be also fine, if that is a configurable option of `catalog.load_table()`)

**Remark:**
Obviously we could blame this on an un-maintained Iceberg table, but I think it would be good for the pyIceberg lib to be robust against such scenarios, hence why I opened the issue.

### 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

Bắt đầu trong pyiceberg/table/metadata.py quanh dòng 663, nơi catalog.load_table() sử dụng model_validate_json của Pydantic, và tái hiện mức sử dụng bộ nhớ cực đại với một tệp metadata lớn bằng memray. Điều tra cách các snapshot và schema được giữ lại trong quá trình tải; được xem là hoàn thành khi các tệp metadata lớn được tải với mức sử dụng bộ nhớ cực đại thấp hơn đáng kể, đồng thời snapshot và schema mới nhất vẫn khả dụng.

Do mô hình lập chỉ mục viết ra từ nội dung của issue.