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.

Comienza reproduciendo los errores de validación en ModelTrainer(environment=...) y HyperparameterTuner(random_seed=...) usando SageMaker Python SDK 3.5.0 y los parámetros de pipeline mostrados. Compara estas rutas con el comportamiento de v2 descrito en el issue. Se considera terminado cuando una variable de pipeline puede proporcionar tanto el valor de entorno RANDOM_STATE como random_seed sin errores de validación.

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