[2/n] [Serve] Refactor replica rank to prepare for node local ranks

[abrarsheikh]

Summary

This PR refactors the replica rank system to support multi-dimensional ranking (global, node-level, and local ranks) in preparation for node-local rank tracking. The ReplicaRank object now contains three fields instead of being a simple integer, enabling better coordination of replicas across nodes.

Motivation

Currently, Ray Serve only tracks a single global rank per replica. For advanced use cases like tensor parallelism, model sharding across nodes, and node-aware coordination, we need to track:

• Global rank: Replica's rank across all nodes (0 to N-1)
• Node rank: Which node the replica is on (0 to M-1)
• Local rank: Replica's rank on its specific node (0 to K-1)

This PR lays the groundwork by introducing the expanded ReplicaRank schema while maintaining backward compatibility in feature.

Changes

Core Implementation

• schema.py: Extended ReplicaRank to include node_rank and local_rank fields (currently set to -1 as placeholders)
• replica.py: Updated replica actors to handle ReplicaRank objects
• context.py: Changed ReplicaContext.rank type from Optional[int] to ReplicaRank

Current Behavior

• node_rank and local_rank are set to -1 (placeholder values). Will change in future
• Global rank assignment and management works as before
• All existing functionality is preserved

Breaking Changes

Rank is changing from int to ReplicaRank