**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
"""
    Start a Java Cloud DataFlow batch job. The parameters of the operation
    will be passed to the job.
    **Example**: ::
        default_args = {
            'owner': 'airflow',
            'depends_on_past': False,
            'start_date':
                (2016, 8, 1),
            'email': ['alex@vanboxel.be'],
            'email_on_failure': False,
            'email_on_retry': False,
            'retries': 1,
            'retry_delay': timedelta(minutes=30),
            'dataflow_default_options': {
                'project': 'my-gcp-project',
                'zone': 'us-central1-f',
                'stagingLocation': 'gs://bucket/tmp/dataflow/staging/',
            }
        }
        dag = DAG('test-dag', default_args=default_args)
        task = DataFlowJavaOperator(
            gcp_conn_id='gcp_default',
            task_id='normalize-cal',
            jar='{{var.value.gcp_dataflow_base}}pipeline-ingress-cal-normalize-1.0.jar',
            options={
                'autoscalingAlgorithm': 'BASIC',
                'maxNumWorkers': '50',
                'start': '{{ds}}',