Pipeline parameters different behavior in comparison to v2

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

**Describe the bug**
In v2, it was possible to pass pipeline variables to environment variables into an `Estimator`. For example, an environment variable "RANDOM_STATE" could be set to a value given as a pipeline variable. This does not work in v3 in a `ModelTrainer` instance, resulting in a validation error: `ValidationError: 1 validation error for ModelTrainer`. A similar issue exists in the `HyperparameterTuner` which does not accept a pipeline variable for the `random_seed`, returning `ValidationError: 1 validation error for HyperParameterTuningJobConfig`.

**To reproduce**
Create `ModelTrainer` instance and pass a pipeline parameter into the `environment` argument dictionary.
Create a `HyperparameterTuner` instance and pass a pipeline parameter into the `random_seed` argument.
```
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": self.pipeline_params["RandomState"].to_string(), # <-- This line causes issues
**self.default_env_vars,
},
)

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 line causes issues
tags=self.tags,
)
```

**Expected behavior**
Pipeline variables should be able to affect environment variables, as well as the `random_seed` argument of the `HyperparameterTuner`.

**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.

Commencez par reproduire les erreurs de validation dans ModelTrainer(environment=...) et HyperparameterTuner(random_seed=...) avec SageMaker Python SDK 3.5.0 et les paramètres de pipeline indiqués. Comparez ces chemins avec le comportement de v2 décrit dans l’issue. Le travail est terminé lorsqu’une variable de pipeline peut fournir à la fois la valeur d’environnement RANDOM_STATE et random_seed sans erreurs de validation.

Rédigé par le modèle d'indexation à partir du texte de l'issue.