[PIPELINES] Support partition transforms in SDP partition_cols

spark=4.1.3
iceberg=1.11.0

### Problem

SDP currently treats every entry in `partition_cols` as an identity partition transform.

for example

```python
@dp.materialized_view(partition_cols=["months(event_time)"])
def events():
...
```

causes `months(event_time)` to be interpreted as a column name instead of a partition transform.

Behaviour comes from `DatasetManager.materializeTable` where all partition cols are converted using identity

```scala
table.partitionCols.toSeq.flatten.map(Expressions.identity)
```

and as a result using the above partition col it just becomes `identity('months(event_time)')`.

### Expected behaviour

SDP should support the same partition transforms that Spark SQL already supports

Bare col-names (`partition_col=["event_date"]`) should continue producing `identity(event_date)`
but partition transforms should be respected, and combining partitions like

```python
partition_cols=["country", "bucket(16, user_id)"]
```

should produce its corresponding V2 partition transforms.

### Existing SQL support

Catalyst parses partition transform syntax for SQL `PARTITIONED BY` and creates corresponding V2 `Transform`

SDP should just reuse the existing Catalyst parsing logic and not short-circuit everything to identity transform.

Start in DatasetManager.materializeTable, where partition_cols are currently mapped through Expressions.identity. Read the existing Catalyst parsing logic used for SQL PARTITIONED BY transforms, then verify the behavior for bare columns, months(event_time), and bucket(16, user_id). Done means identity transforms remain for column names while supported transform syntax produces the corresponding V2 transforms.

Written by the indexing model from the issue text.