**Apache Airflow version**: 2.0.0rc3

**Kubernetes version (if you are using kubernetes)** (use `kubectl version`):

**Environment**:

- **Cloud provider or hardware configuration**: Linux
- **OS** (e.g. from /etc/os-release): Ubuntu
- **Kernel** (e.g. `uname -a`): 
- **Install tools**:
- **Others**:

**What happened**:

After shutting down the scheduler, while tasks were in running state, trying to restart the scheduler results in pk violations..

```[2020-12-15 22:43:29,673] {scheduler_job.py:1293} ERROR - Exception when executing SchedulerJob._run_scheduler_loop
Traceback (most recent call last):
  File "/home/jcoder/git/airflow_2.0/pyenv/lib/python3.7/site-packages/sqlalchemy/engine/base.py", line 1277, in _execute_context
    cursor, statement, parameters, context
  File "/home/jcoder/git/airflow_2.0/pyenv/lib/python3.7/site-packages/sqlalchemy/engine/default.py", line 593, in do_execute
    cursor.execute(statement, parameters)
psycopg2.errors.UniqueViolation: duplicate key value violates unique constraint "dag_run_dag_id_run_id_key"
DETAIL:  Key (dag_id, run_id)=(example_task_group, scheduled__2020-12-14T04:31:00+00:00) already exists.
```


**What you expected to happen**:

Scheduler restarts and picks up where it left off.

**How to reproduce it**:

Set example dag ( I used task_group) to schedule_interval `* * * * *` and start the scheduler and let it run for a few minutes. 
Shut down the scheduler
Attempt to restart the scheduler

**Anything else we need to know**:
I came across this doing testing using the LocalExecutor in a virtual env. If no else is able to reproduce it, I'll try again in a clean virtual env.

airflow/jobs/scheduler_job.py
"""
    This SchedulerJob runs for a specific time interval and schedules the jobs
    that are ready to run. It figures out the latest runs for each
    task and sees if the dependencies for the next schedules are met.
    If so, it creates appropriate TaskInstances and sends run commands to the
    executor. It does this for each task in each DAG and repeats.
    :param dag_id: if specified, only schedule tasks with this DAG ID
    :type dag_id: str
    :param dag_ids: if specified, only schedule tasks with these DAG IDs
    :type dag_ids: list[str]
    :param subdir: directory containing Python files with Airflow DAG
        definitions, or a specific path to a file
    :type subdir: str
    :param num_runs: The number of times to run the scheduling loop. If you
        have a large number of DAG files this could complete before each file
        has been parsed. -1 for unlimited times.
    :type num_runs: int
    :param num_times_parse_dags: The number of times to try to parse each DAG file.
        -1 for unlimited times.
    :type num_times_parse_dags: int
    :param processor_poll_interval: The number of seconds to wait between
        polls of running processors
    :type processor_poll_interval: int
    :param do_pickle: once a DAG object is obtained by executing the Python
        file, whether to serialize the DAG object to the DB
    :type do_pickle: bool
    """
    __mapper_args__ = {'polymorphic_identity': 'SchedulerJob'}
    heartrate: int = conf.getint('scheduler', 'SCHEDULER_HEARTBEAT_SEC')
    def __init__(