**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
"""
    Returns the last dag run for a dag, None if there was none.
    Last dag run can be any type of run eg. scheduled or backfilled.
    Overridden DagRuns are ignored.
    """
    DR = DagRun
    query = session.query(DR).filter(DR.dag_id == dag_id)
    if not include_externally_triggered:
        query = query.filter(DR.external_trigger == False)  
    query = query.order_by(DR.execution_date.desc())
    return query.first()
@functools.total_ordering
class DAG(LoggingMixin):
    """
    A dag (directed acyclic graph) is a collection of tasks with directional
    dependencies. A dag also has a schedule, a start date and an end date
    (optional). For each schedule, (say daily or hourly), the DAG needs to run
    each individual tasks as their dependencies are met. Certain tasks have
    the property of depending on their own past, meaning that they can't run
    until their previous schedule (and upstream tasks) are completed.
    DAGs essentially act as namespaces for tasks. A task_id can only be
    added once to a DAG.
    :param dag_id: The id of the DAG; must consist exclusively of alphanumeric
        characters, dashes, dots and underscores (all ASCII)
    :type dag_id: str
    :param description: The description for the DAG to e.g. be shown on the webserver
    :type description: str
    :param schedule_interval: Defines how often that DAG runs, this
        timedelta object gets added to your latest task instance's
        execution_date to figure out the next schedule