**What happened**:

With the new Backport providers, when calling `DataflowCreatePythonJobOperator` with an empty `py_rerquirments` Dataflow commands always launch inside a virtualenv.

**What you expected to happen**:

The Dataflow command should run locally.


airflow/providers/google/cloud/operators/dataflow.py
'zone': 'europe-west1-d',
               'stagingLocation': 'gs://my-staging-bucket/staging/'
           }
       }
    You need to pass the path to your dataflow as a file reference with the ``jar``
    parameter, the jar needs to be a self executing jar (see documentation here:
    https://beam.apache.org/documentation/runners/dataflow/#self-executing-jar).
    Use ``options`` to pass on options to your job.
    .. code-block:: python
       t1 = DataFlowJavaOperator(
           task_id='dataflow_example',
           jar='{{var.value.gcp_dataflow_base}}pipeline/build/libs/pipeline-example-1.0.jar',
           options={
               'autoscalingAlgorithm': 'BASIC',
               'maxNumWorkers': '50',
               'start': '{{ds}}',
               'partitionType': 'DAY',
               'labels': {'foo' : 'bar'}
           },
           gcp_conn_id='airflow-conn-id',
           dag=my-dag)
    """
    template_fields = ['options', 'jar', 'job_name']
    ui_color = '#0273d4'
    
    @apply_defaults
    def __init__(self, *,
                 jar: str,
                 job_name: str = '{{task.task_id}}',
                 dataflow_default_options: Optional[dict] = None,