**Apache Airflow version**: 2.0.0


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

**Environment**: 

- **Cloud provider or hardware configuration**: local/aws
- **OS** (e.g. from /etc/os-release): Ubuntu 18.04.5 LTS
- **Kernel** (e.g. `uname -a`): 5.4.0-1032-aws
- **Install tools**: pip
- **Others**:

**What happened**:
I did a fresh Airflow 2.0.0 install. With this version, when I manually trigger a DAG, Airflow skips the next scheduled run.
<!-- (please include exact error messages if you can) -->

**What you expected to happen**:
Manual runs do not interfere with the scheduled runs prior to Airflow 2. 
<!-- What do you think went wrong? -->

**How to reproduce it**:
Create a simple hourly DAG. After enabling it and the initial run, run it manually. It shall skip the next hour. Below is an example, where the manual run with execution time of 08:17 causes the scheduled run with execution time of 08:00 to skip. 
![image](https://user-images.githubusercontent.com/26160471/103457719-c7193480-4d12-11eb-82cb-42efaedc9ef4.png)


airflow/models/dag.py
self, dag: DAG, most_recent_dag_run: Optional[pendulum.DateTime], active_runs_of_dag: int
    ) -> None:
        """
        Calculate ``next_dagrun`` and `next_dagrun_create_after``
        :param dag: The DAG object
        :param most_recent_dag_run: DateTime of most recent run of this dag, or none if not yet scheduled.
        :param active_runs_of_dag: Number of currently active runs of this dag
        """
        self.next_dagrun, self.next_dagrun_create_after = dag.next_dagrun_info(most_recent_dag_run)
        if dag.max_active_runs and active_runs_of_dag >= dag.max_active_runs:
            
            log.debug(
                "DAG %s is at (or above) max_active_runs (%d of %d), not creating any more runs",
                dag.dag_id,
                active_runs_of_dag,
                dag.max_active_runs,
            )
            self.next_dagrun_create_after = None
        log.info("Setting next_dagrun for %s to %s", dag.dag_id, self.next_dagrun)
def dag(*dag_args, **dag_kwargs):
    """
    Python dag decorator. Wraps a function into an Airflow DAG.
    Accepts kwargs for operator kwarg. Can be used to parametrize DAGs.
    :param dag_args: Arguments for DAG object
    :type dag_args: list
    :param dag_kwargs: Kwargs for DAG object.
    :type dag_kwargs: dict
    """
    def wrapper(f: Callable):