Naming discrepancy between `env` in Processing and `environment` in Estimator

# Describe the feature you'd like
There is a discrepancy on passing environment variables in Processing and Estimator. The parameter is called `env` in Processing and `environment` in Estimator.

I would like these to be aligned. For backwards compatibility sake, this should probably be manifested through a `environment_variables` parameter, but any solution would work for me.

## The problem
The problem is that we cannot have a unified interface to these entities using `**kwargs` to pass arguments without manually parsing a parameter ourselves.

Current situation van be something like this if we use `env` for both situations:

```python
def data_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)

return Processing(**kwargs)

def model_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)

env_vars = kwargs.pop("env")
if env_vars:
kwargs["environment"] = env_vars

return Estimator(**kwargs)

data_processing(environment="dev", env={"MY_VAR": 42})
model_training(environment="dev", env={"MY_VAR": 67})
```

Or with a more compatible interface

```python
def data_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)

env_vars = kwargs.pop("environment_variables")
if env_vars:
kwargs["env"] = env_vars

return Processing(**kwargs)

def model_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)

env_vars = kwargs.pop("environment_variables")
if env_vars:
kwargs["environment"] = env_vars

return Estimator(**kwargs)

data_processing(environment="dev", environment_variables={"MY_VAR": 42})
model_training(environment="dev", environment_variables={"MY_VAR": 67})
```

Ideally, we would want it to look like this because both classes accept a `environment_variables` parameter:

```python
def data_processing(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)

return Processing(**kwargs)

def model_training(environment: Literal["dev", "preprod", "prod"], **kwargs):
initialize_environment(environment)

return Estimator(**kwargs)

data_processing(environment="dev", environment_variables={"MY_VAR": 42})
model_training(environment="dev", environment_variables={"MY_VAR": 67})
```

Beginne damit, die Einstiegspunkte von Processing und Estimator sowie deren vorhandene Parameter für Umgebungsvariablen zu lokalisieren. Vergleiche, wie `env` und `environment` bereitgestellt werden, und bestimme anschließend eine rückwärtskompatible gemeinsame Schnittstelle; abgeschlossen ist die Aufgabe, wenn beide Entitäten den angeglichenen Parameter akzeptieren, ohne die bestehende Nutzung zu beeinträchtigen.

Vom Indexierungsmodell aus dem Issue-Text verfasst.