KEP-3562: [OptimizationJob] Support Multi-Objective Optimization in API and Suggestion Engine

### What you would like to be added?

Context:
Phase 1 of OptimizationJob in [KEP-3562](https://github.com/kubeflow/trainer/pull/3565) restricts Objectives to exactly 1 metric (MinItems=1, MaxItems=1).

Problem:
Real-world ML problems often require balancing trade-offs between competing objectives (e.g., maximizing accuracy while minimizing model latency or memory usage).

Future Goals:

- Update CEL validation on `OptimizationJobSpec.objectives` to allow multiple metrics.

- Extend `Optuna` gRPC adapter for multi-objective optimization.

- Update `OptimizationJobStatus` to track multi-metric optimal results.

Relates to KEP-3562 Master Tracking [Issue](https://github.com/kubeflow/trainer/issues/3562).

### Why is this needed?

Multi-objective support allows users to optimize production trade-offs (e.g., model size vs. throughput) directly within a single OptimizationJob.

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Start with KEP-3562 and its master tracking issue to understand the existing single-objective OptimizationJob design. Then trace the objectives CEL validation, the Optuna gRPC adapter, and OptimizationJobStatus. Done means accepting multiple metrics, supporting multi-objective optimization, and representing multi-metric optimal results.

Written by the indexing model from the issue text.