If you have a DAG with `max_active_runs=1` and it is running some tasks, and then you manually trigger another run of this DAG, both runs end up not scheduling anything and you see this message over and over in the logs:

```
[2020-10-16 14:00:34,118]  2437572 {{airflow.jobs.scheduler_job.SchedulerJob scheduler_job.py:1657}} INFO - DAG scenario1_case2_01_1 already has 1 active runs, not queuing any more tasks
```

A longer term fix for this would be to introduce a "queued" state for DagRuns, and then when manually creating dag runs set it to queued, and only have the scheduler set DagRuns to running.

That may or may not be less work than fixing the current mechanism.
airflow/jobs/scheduler_job.py
"""
    Process a Python file containing Airflow DAGs.
    This includes:
    1. Execute the file and look for DAG objects in the namespace.
    2. Pickle the DAG and save it to the DB (if necessary).
    3. For each DAG, see what tasks should run and create appropriate task
    instances in the DB.
    4. Record any errors importing the file into ORM
    5. Kill (in ORM) any task instances belonging to the DAGs that haven't
    issued a heartbeat in a while.
    Returns a list of SimpleDag objects that represent the DAGs found in
    the file
    :param dag_ids: If specified, only look at these DAG ID's
    :type dag_ids: List[str]
    :param log: Logger to save the processing process
    :type log: logging.Logger
    """
    UNIT_TEST_MODE: bool = conf.getboolean('core', 'UNIT_TEST_MODE')
    def __init__(self, dag_ids: Optional[List[str]], log: logging.Logger):