Machine Learning Feature Store Support for Apache Gravitino

### Describe the feature

Attached document proposes adding **ML Feature Store** support to Gravitino—enabling Gravitino to serve as a unified metadata management layer for ML features, bridging the gap between data engineering and machine learning workflows. Feature store metadata is the natural complement to model metadata: models consume features, and features are derived from underlying data assets that Gravitino already manages.

[ML feature store proposal document](https://docs.google.com/document/d/11NYd3KWxryCFxuG887kJ3d0p87leNyqK/edit?usp=sharing&ouid=114123410449326225963&rtpof=true&sd=true)

The doc contains details on:
**Motivation** — Why Gravitino is uniquely positioned (federated metadata, existing model catalog, governance infra)
**Metadata Model** — `FeatureEntity`, `FeatureGroup`, `Feature`, `FeatureGroupVersion`, `FeatureView` with full field definitions
**API Design** — `FeatureStoreCatalog` Java interface with CRUD for all entities, following Gravitino's dispatcher/change-object patterns
**REST API** — Full endpoint specification with example request/response JSON
**Java & Python SDKs** — Code examples showing end-to-end workflows
**Storage** — Database table schemas for MySQL/PostgreSQL/H2 persistence
**Federated Connectors** — Architecture for Feast, Tecton, Hopsworks, SageMaker connectors (Feast as priority)
**Model Catalog Integration** — Feature-model lineage and impact analysis
**Engine Connectors** — Spark, Trino, and Python framework integration
**Governance** — RBAC privileges, tag-based governance, audit events
**CLI & Web UI** — Command examples and UI wireframe descriptions
**Implementation Plan** — 6-phase rollout with task breakdowns
**Testing Strategy** — Unit and integration test plan following Gravitino conventions

### Motivation

Machine learning teams face a critical challenge: managing the lifecycle of **features**—the processed, structured data inputs that ML models consume. In production environments, feature engineering represents 60-80% of the effort in building ML systems, yet features remain one of the least managed assets in the data ecosystem.

### Describe the solution

Add a new FEATURE catalog type to Gravitino that manages feature metadata (not data) — acting as a unified registry and governance layer.

### Additional context

_No response_

Start with the linked ML feature store proposal, then compare its FeatureStoreCatalog interface and dispatcher/change-object patterns with Gravitino's existing model catalog. Review the proposed REST, SDK, storage, connector, governance, and testing sections; the work is done only when the agreed rollout scope and implementation plan are completed.

Written by the indexing model from the issue text.