chore: standardize unsupported model error #2517 (#2518)

# What does this PR do?

- llama_stack/exceptions.py: Add UnsupportedModelError class
- remote inference ollama.py and utils/inference/model_registry.py:
Changed ValueError in favor of UnsupportedModelError
- utils/inference/litellm_openai_mixin.py: remove `register_model`
function implementation from `LiteLLMOpenAIMixin` class. Now uses the
parent class `ModelRegistryHelper`'s function implementation

Closes #2517


## Test Plan


1. Create a new `test_run_openai.yaml` and paste the following config in
it:

```yaml
version: '2'
image_name: test-image
apis:
- inference
providers:
  inference:
  - provider_id: openai
    provider_type: remote::openai
    config:
      max_tokens: 8192
models:
- metadata: {}
  model_id: "non-existent-model"
  provider_id: openai
  model_type: llm
server:
  port: 8321
```

And run the server with:
```bash
uv run llama stack run test_run_openai.yaml
```

You should now get a `llama_stack.exceptions.UnsupportedModelError` with
the supported list of models in the error message.

---

Tested for the following remote inference providers, and they all raise
the `UnsupportedModelError`:
- Anthropic
- Cerebras
- Fireworks
- Gemini
- Groq
- Ollama
- OpenAI
- SambaNova
- Together
- Watsonx

---------

Co-authored-by: Rohan Awhad <rawhad@redhat.com>

llama_stack/providers/utils/inference/litellm_openai_mixin.py

@@ -13,6 +13,7 @@ from llama_stack.apis.common.content_types import (
     InterleavedContent,
     InterleavedContentItem,
 )
+from llama_stack.apis.common.errors import UnsupportedModelError
 from llama_stack.apis.inference import (
     ChatCompletionRequest,
     ChatCompletionResponse,
@@ -92,7 +93,7 @@ class LiteLLMOpenAIMixin(
     async def register_model(self, model: Model) -> Model:
         model_id = self.get_provider_model_id(model.provider_resource_id)
         if model_id is None:
-            raise ValueError(f"Unsupported model: {model.provider_resource_id}")
+            raise UnsupportedModelError(model.provider_resource_id, self.alias_to_provider_id_map.keys())
         return model
 
     def get_litellm_model_name(self, model_id: str) -> str: