Add two HTTP endpoints (GET/POST /v1/checkpoints/{name}/{metas,load-metas})
and a standalone `python -m checkpoint_engine.join_cli` entrypoint, so a
new ParameterServer instance can join an existing P2P weight world over
mooncake RDMA without re-reading checkpoints from disk.

Motivation: in elastic-rollout setups (e.g. mshrl), a long-running training
job already holds pinned CPU weight buffers registered with the mooncake
P2PStore. Newly-started inference replicas should be able to pull these
weights over RDMA instead of re-converting the checkpoint from disk.

Changes:
* api.py: GET /v1/checkpoints/{name}/metas returns pickle.dumps(ps.get_metas())
  as application/octet-stream; POST /v1/checkpoints/{name}/load-metas accepts
  the same bytes and feeds them into ps.load_metas(). Bad pickle is rejected
  with 400; PS errors are surfaced as 500.
* join_cli.py: `python -m checkpoint_engine.join_cli` -- the join() flow from
  examples/update.py, packaged as a first-class CLI under the published
  package so consumers can invoke it without checking out the source tree.
  Accepts metas from either a local pickle file or a remote HTTP URL.
* tests/test_api.py: 6 CPU-only tests covering pickle round-trip, ps-error
  propagation, bad-input rejection, and a GET-then-POST chain that validates
  the new endpoints are mutually consistent.

Verified end-to-end on a 2-node launchpad job: 14.5 GiB Qwen2.5-7B weights
transferred from main to elastic in 1.49s over real RDMA (4 mlx5_bond HCAs)
vs 6.49s over TCP fallback in environments without RDMA passthrough.

Replace pickle with pydantic TypeAdapter(dict[int, MemoryBufferMetaList])
for the metas wire format across the HTTP endpoints, join_cli, and
examples/update.py. This reuses the existing pydantic schema (torch.dtype
/ torch.Size already have serializers in data_types.py), removes the
arbitrary-code-execution risk of pickle.loads on request bodies, and makes
the metas self-describing for cross-language consumers.

- api.py: GET /metas returns application/json; POST /load-metas validates
  via validate_json and returns 400 on ValidationError (was broad except).
- join_cli.py / examples/update.py: read/write metas as JSON; document the
  --metas-url HTTP path alongside --load-metas-file.
- tests/test_api.py: use real MemoryBufferMetaList fixtures; add a
  schema-mismatch case (valid JSON, wrong shape -> 400).

join_cli was a duplicate of examples/update.py:join() with an extra
--metas-url flag. Add the flag to update.py, remove join_cli.