docs: add multi-node inference without Ray (mp backend) guide

## Summary

KServe v0.18 added support for multi-node InferenceService deployments **without requiring Ray**, using vLLM's `mp` (multiprocessing) distributed executor backend. The current multi-node documentation at `docs/model-serving/generative-inference/multi-node/multi-node.md` only covers Ray-based multi-node inference.

## What needs to be documented

- The `multinode/executor-backend` annotation on ServingRuntime spec (`ray` vs `mp` mode)
- How `mp` mode derives the number of nodes from pipeline parallelism (PP) and GPUs per node from tensor parallelism (TP)
- The worker headless service for pod DNS discovery
- Environment variables injected into worker containers (`PIPELINE_PARALLEL_SIZE`, `TENSOR_PARALLEL_SIZE`, `WORKER_SVC`)
- Example YAML for a multi-node InferenceService using `mp` backend

## Related PR

- https://github.com/kserve/kserve/pull/5366

## Since

KServe v0.18

Start with docs/model-serving/generative-inference/multi-node/multi-node.md and the related PR #5366 to understand the existing Ray-based guide. Document the mp annotation, PP/TP sizing, worker headless service, injected environment variables, and add a complete multi-node InferenceService YAML example; done means the guide covers KServe v0.18 mp deployments without Ray.

Written by the indexing model from the issue text.