ModelTrainer and HyperparameterTuner missing environment variables

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

**Describe the bug**
Tuning job does not add environment variables to the training jobs it creates. No environment variables are set, despite the environment variables being defined in the ModelTrainer correctly. The HyperparameterTuner does not seem to correctly propagate them.

**To reproduce**
Inside of a `TuningStep` pipeline step, use a `ModelTrainer` and add environment variables via the `environment` argument. Then define a `HyperparameterTuner` and pass the `model_trainer` object to it. Do all this inside of a pipeline session, such that `.tune()` will return the Arguments for the `TuningStep`. The resulting JSON of the pipeline definition will not contain an `"Environment"` key with environment variables inside the `"TrainingJobDefinition"` key within the `"Arguments"` for the tuning step.
```
model = ModelTrainer(
source_code=source_code,
compute=compute,
networking=self.networking,
base_job_name=base_job_name,
training_image=self.image_uris["train"],
output_data_config=OutputDataConfig(
s3_output_path=Join(
on="/",
values=[
self.s3_uri_runtime,
ExecutionVariables.PIPELINE_EXECUTION_ID,
"02_mt_output",
],
),
kms_key_id=self.aws_params["kms_key_hub"],
),
stopping_condition=StoppingCondition(max_runtime_in_seconds=28800),
role=self.aws_params["exec_role"],
sagemaker_session=self.pipeline_session,
environment={
"RANDOM_STATE": "42",
**self.default_env_vars,
},
# tags=self.tags_special,
)

hyperparameter_tuner = HyperparameterTuner(
model_trainer=model,
base_tuning_job_name=base_job_name,
metric_definitions=metric_definitions,
objective_metric_name=self.hpt_params["objective_metric_name"],
objective_type=self.hpt_params["objective_type"],
hyperparameter_ranges=self.hpt_params["hyperparameter_ranges"],
max_jobs=self.hpt_params["max_jobs"],
strategy="Bayesian",
max_parallel_jobs=4,
# random_seed=self.pipeline_params["RandomState"], # this is another bug, for another issue.
random_seed=42,
tags=self.tags,
)
```

**Expected behavior**
The environment variables passed as an argument to the `ModelTrainer` should be set in the training jobs created by a tuning job that uses this model trainer.

**System information**
A description of your system. Please provide:
- **SageMaker Python SDK version**: 3.5.0

**Additional context**
This is a roadblock for us regarding a migration from v2 to v3.

Start with the ModelTrainer, HyperparameterTuner, and TuningStep entry points used in the reproduction, then inspect how the model trainer is serialized into the tuning step's TrainingJobDefinition. Reproduce the pipeline JSON and verify that the Environment key contains the ModelTrainer variables for the result to be complete.

Written by the indexing model from the issue text.