Windows host writes sm_train.sh with CRLF in SDK v3, causing SageMaker training job bootstrap failure ($'\r': command not found)

**PySDK Version**
- [ ] PySDK V2 (2.x)
- [x] PySDK V3 (3.x)

**Describe the bug**
When launching a SageMaker training job from Windows using `sagemaker.train.ModelTrainer` (SDK v3), the generated bootstrap script `sm_train.sh` is written with CRLF line endings. Inside the Linux training container, bash fails to parse it and the job exits before user training code starts.

The script appears to be written in `sagemaker/train/model_trainer.py` with:

`with open(os.path.join(tmp_dir.name, TRAIN_SCRIPT), "w") as f:`

which applies platform newline conversion on Windows (`\r\n`).

**To reproduce**
1. Use Windows host with SageMaker Python SDK v3.
2. Run this complete script (replace `ROLE_ARN` and `S3_INPUT`):

```python
from sagemaker.core import image_uris
from sagemaker.core.helper.session_helper import Session
from sagemaker.core.training.configs import SourceCode, Compute, InputData
from sagemaker.train import ModelTrainer

session = Session()
region = session.boto_region_name

training_image = image_uris.retrieve(
framework="pytorch",
region=region,
version="2.4.0",
py_version="py311",
instance_type="ml.g4dn.xlarge",
image_scope="training",
)

trainer = ModelTrainer(
sagemaker_session=session,
role="ROLE_ARN",
training_image=training_image,
source_code=SourceCode(
source_dir=".",
entry_script="sagemaker_entry.py",
requirements="requirements/sagemaker_train.txt",
),
compute=Compute(instance_type="ml.g4dn.xlarge", instance_count=1),
)

trainer.train(
input_data_config=[InputData(channel_name="train", data_source="S3_INPUT")],
wait=True,
logs=True,
)
```

3. Check CloudWatch logs for the training job.

**Expected behavior**
`sm_train.sh` should be written with LF (`\n`) and execute correctly in the Linux container, allowing the training entry point to start.

**Screenshots or logs**
Observed logs:

```text
/opt/ml/input/data/sm_drivers/sm_train.sh: line 1: $'\r': command not found
Starting training script#015
/opt/ml/input/data/sm_drivers/sm_train.sh: line 3: set: -#015: invalid option
set: usage: set [-abefhkmnptuvxBCHP] [-o option-name] [--]
/opt/ml/input/data/sm_drivers/sm_train.sh: line 5: $'\r': command not found
/opt/ml/input/data/sm_drivers/sm_train.sh: line 6: syntax error near unexpected token `$'{\r''
/opt/ml/input/data/sm_drivers/sm_train.sh: line 6: `handle_error() {#015'
```

**System information**
- **SageMaker Python SDK version**: 3.12.0
- **Framework name (eg. PyTorch) or algorithm (eg. KMeans)**: PyTorch
- **Framework version**: 2.4.0 (DLC)
- **Python version**: 3.11
- **CPU or GPU**: GPU (`ml.g4dn.xlarge`)
- **Custom Docker image (Y/N)**: N

**Additional context**
- Reproduced from Windows 10 host.
- Workaround: launching from Linux (WSL/Studio/EC2) avoids CRLF in `sm_train.sh`.
- Suggested fix in SDK: force LF on script write, e.g. `open(..., "w", newline="\n")`.

Esamina il codice di scrittura dello script in sagemaker/train/model_trainer.py, in particolare la chiamata open mostrata nell’issue, e riproduci il file generato su Windows. Il lavoro è completato quando sm_train.sh contiene terminatori di riga LF e i log del container SageMaker mostrano che l’entry point del training si avvia senza gli errori di parsing di bash segnalati.

Scritto dal modello di indicizzazione a partire dal testo della issue.