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

Commencez dans pyiceberg/table/metadata.py vers la ligne 663, où catalog.load_table() utilise model_validate_json de Pydantic, et reproduisez l’utilisation maximale de la mémoire avec un fichier de métadonnées volumineux en utilisant memray. Étudiez la manière dont les snapshots et les schemas sont conservés pendant le chargement ; le travail sera considéré comme terminé lorsque les fichiers de métadonnées volumineux se chargeront avec une utilisation maximale de la mémoire nettement inférieure, tout en conservant le dernier snapshot et le dernier schema disponibles.

Rédigé par le modèle d'indexation à partir du texte de l'issue.