HyperparameterTuner does not preserve `disable_output_compression` setting of Estimator

**Describe the bug**
When setting `disable_output_compression=True` in a PyTorch SageMaker Estimator that is used with HyperparameterTuner, the resulting HPO training jobs still compress the model output. This parameter is correctly set in the estimator but appears to be ignored when the estimator is used within a hyperparameter tuning job.

**To reproduce**
1. Create a PyTorch estimator with `disable_output_compression=True`
2. Use this estimator with a `HyperparameterTuner`
3. Launch the hyperparameter tuning job
4. Observe that the model outputs in the resulting training jobs are still compressed

Here's a minimal code example that reproduces the issue:

```python
import sagemaker
from sagemaker.pytorch.estimator import PyTorch
from sagemaker.tuner import HyperparameterTuner, ContinuousParameter

# Set up the PyTorch estimator with disable_output_compression=True
role = "arn:aws:iam::123456789012:role/SageMakerRole"
estimator = PyTorch(
disable_output_compression=True, # This should disable compression
entry_point="train.py",
source_dir="./code",
role=role,
instance_count=1,
instance_type="ml.m5.xlarge",
py_version="py3",
)

# Set up the hyperparameter tuner
hyperparameter_ranges = {
"learning-rate": ContinuousParameter(0.001, 0.1)
}

tuner = HyperparameterTuner(
estimator=estimator,
objective_metric_name="validation-accuracy",
hyperparameter_ranges=hyperparameter_ranges,
metric_definitions=[
{
"Name": "validation-accuracy",
"Regex": "validation accuracy: ([0-9\\.]+)"
}
],
max_jobs=2,
max_parallel_jobs=2
)

# Launch the tuning job
tuner.fit({"train": "s3://bucket/path/to/training/data"})
```

Expected behavior
When `disable_output_compression=True` is set in the PyTorch estimator, all training jobs created by the `HyperparameterTuner` should respect this setting and not compress the model output.

Screenshots or logs
When examining the training jobs created by the hyperparameter tuning job, the model artifacts are still compressed despite setting `disable_output_compression=True` in the estimator.

System information

* SageMaker Python SDK version: 2.246.0
* Framework name: PyTorch
* Framework version: 2.3.0
* Python version: 3.10
* CPU or GPU: CPU
* Custom Docker image: Yes, derived from `pytorch-training:2.3.0-cpu-py311-ubuntu20.04-sagemaker`

**Additional context**

This issue appears to be specific to hyperparameter tuning jobs. When using the same PyTorch estimator directly (not through a HyperparameterTuner), the disable_output_compression=True parameter works as expected and the model output is not compressed.

The issue might be that the disable_output_compression parameter is not being properly propagated from the estimator to the individual training jobs created by the HyperparameterTuner.

Inizia tracciando il modo in cui HyperparameterTuner costruisce la configurazione del singolo job di training a partire dal PyTorch Estimator, quindi confrontala con la configurazione utilizzata quando l’estimator viene eseguito direttamente. Verifica che disable_output_compression=True venga mantenuto nei job di tuning e conferma il completamento osservando artefatti del modello non compressi nello scenario riprodotto.

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