ModelBuilder with source_code + DJL LMI: /opt/ml/model becomes read-only, breaking HF Hub model downloads

## Description

When using `ModelBuilder` (SDK v3) with a pre-built DJL LMI container image and `source_code` (via `SourceCode`) to provide custom `requirements.txt`, the model directory `/opt/ml/model/` becomes read-only at runtime. This prevents the DJL container from downloading models from HuggingFace Hub, which tries to write cache files to `/opt/ml/model/`.

Additionally, `ModelBuilder` overrides user-provided `HF_MODEL_ID` environment variable with the value from the `model=` parameter, making it impossible to point the container to the local model path (`/opt/ml/model`) when S3 model artifacts are also provided via `s3_model_data_url`.

## How to Reproduce

```python
from sagemaker.serve import ModelBuilder, ModelServer
from sagemaker.serve.builder.schema_builder import SchemaBuilder
from sagemaker.serve.mode.function_pointers import Mode
from sagemaker.serve.model_builder import SourceCode

source_code = SourceCode(
source_dir="./model_code",
requirements="requirements.txt", # e.g. transformers>=4.55.0
)

mb = ModelBuilder(
model="chromadb/context-1", # HF Hub model ID
role_arn=ROLE,
image_uri="763104351884.dkr.ecr.us-east-1.amazonaws.com/djl-inference:0.36.0-lmi22.0.0-cu129",
model_server=ModelServer.DJL_SERVING,
schema_builder=SchemaBuilder(
{"inputs": "Hello", "parameters": {"max_new_tokens": 64}},
[{"generated_text": "Hi"}],
),
source_code=source_code,
env_vars={"OPTION_TENSOR_PARALLEL_DEGREE": "4", ...},
instance_type="ml.g6e.12xlarge",
mode=Mode.SAGEMAKER_ENDPOINT,
)

model = mb.build()
endpoint = mb.deploy(endpoint_name="test", wait=True)
# FAILS: OSError: [Errno 30] Read-only file system: /opt/ml/model/models--chromadb--context-1
```

## Observed Behavior

1. `ModelBuilder.build()` packages the `source_code` directory into a `model.tar.gz` and uploads to S3
2. At deploy time, SageMaker mounts this tar.gz at `/opt/ml/model/` — which becomes **read-only**
3. `ModelBuilder` sets `HF_MODEL_ID=chromadb/context-1` (from `model=`), overriding any user-provided value
4. DJL LMI container sees `HF_MODEL_ID=chromadb/context-1` and tries to download from HF Hub
5. HF Hub download tries to write cache to `/opt/ml/model/models--chromadb--context-1/`
6. Fails with `OSError: [Errno 30] Read-only file system`

CloudWatch logs confirm:
```
OSError: [Errno 30] Read-only file system: /opt/ml/model/models--chromadb--context-1
```

## Expected Behavior

Users should be able to use `ModelBuilder` with:
- A pre-built container image (e.g. DJL LMI)
- `source_code` with a custom `requirements.txt` to install additional dependencies at container startup
- A HuggingFace Hub model ID that the container downloads at runtime

The `requirements.txt` installation should not make `/opt/ml/model/` read-only, or the HF Hub cache should be redirected to a writable location (e.g. `/tmp`).

## Workaround Attempted

Setting `HF_HOME=/tmp/hf_home` and `HUGGINGFACE_HUB_CACHE=/tmp/hf_home/hub` in `env_vars` — these appear in the container environment but the DJL container still writes to `/opt/ml/model/`.

## Use Case

This is a common pattern for deploying newer models (e.g. OpenAI GPT-OSS based models like `chromadb/context-1`) that require a newer `transformers` version than what is bundled in the DJL LMI container. The `source_code` with `requirements.txt` is the natural SDK v3 mechanism for this, but it is incompatible with HF Hub model downloads.

## Environment

- SageMaker Python SDK: 3.6.0
- Container: `djl-inference:0.36.0-lmi22.0.0-cu129`
- Instance: `ml.g6e.12xlarge`
- Region: `us-east-1`

Start at ModelBuilder.build() and deploy(), along with SourceCode packaging, and reproduce the issue with the DJL LMI image and the supplied model configuration. Trace how source_code is packaged and how HF_MODEL_ID and env_vars are applied. Done means runtime model downloads can write to a writable location and a user-provided HF_MODEL_ID is not unexpectedly replaced.

Escrito por el modelo de indexación a partir del texto del issue.