[Feature] Support materialized column to improve query performance for complex types

### Search before asking

- [X] I searched in the [issues](https://github.com/apache/incubator-paimon/issues) and found nothing similar.

### Motivation

In the world of data warehouse, it is very common to use one or more columns from a complex type such as a map, or to put many subfields into it. These operations can greatly affect query performance because:

1. These operations are very wasteful IO. For example, if we have a field type of Map, which contains dozens of subfields, we need to read the entire column when reading this column. And Spark will traverse the entire map to get the value of the target key.
2. Cannot take advantage of vectorized reads when reading nested type columns.
3. Filter pushdown cannot be used when reading nested columns.

It is necessary to introduce the materialized column feature in Flink Table Store, which transparently solves the above problems of arbitrary columnar storage (not just Parquet).

### Solution

_No response_

### Anything else?

_No response_

### Are you willing to submit a PR?

- [ ] I'm willing to submit a PR!

The issue names no files, tests, or entry points, and provides motivation but no concrete solution. Start by locating Paimon's columnar storage and complex-type handling, then determine the materialized-column design and its scope across supported readers. Done should include an agreed implementation plan, behavior for nested or map fields, and validation of query-performance benefits.

Written by the indexing model from the issue text.