feat(ollama): periodically refresh models (#2805)

For self-hosted providers like Ollama (or vLLM), the backing server is
running a set of models. That server should be treated as the source of
truth and the Stack registry should just be a cache for those models. Of
course, in production environments, you may not want this (because you
know what model you are running statically) hence there's a config
boolean to control this behavior.

_This is part of a series of PRs aimed at removing the requirement of
needing to set `INFERENCE_MODEL` env variables for running Llama Stack
server._

## Test Plan

Copy and modify the starter.yaml template / config and enable
`refresh_models: true, refresh_models_interval: 10` for the ollama
provider. Then, run:

```
LLAMA_STACK_LOGGING=all=debug \
  ENABLE_OLLAMA=ollama uv run llama stack run --image-type venv /tmp/starter.yaml
```

See a gargantuan amount of logs, but verify that the provider is
periodically refreshing models. Stop and prune a model from ollama
server, restart the server. Verify that the model goes away when I call
`uv run llama-stack-client models list`

llama_stack/apis/inference/inference.py

@@ -819,6 +819,12 @@ class OpenAIEmbeddingsResponse(BaseModel):
 class ModelStore(Protocol):
     async def get_model(self, identifier: str) -> Model: ...
 
+    async def update_registered_models(
+        self,
+        provider_id: str,
+        models: list[Model],
+    ) -> None: ...
+
 
 class TextTruncation(Enum):
     """Config for how to truncate text for embedding when text is longer than the model's max sequence length. Start and End semantics depend on whether the language is left-to-right or right-to-left.