**Apache Airflow version**: 1.10.14

**Environment**:

- **Cloud provider or hardware configuration**: docker
- **OS** (e.g. from /etc/os-release): apache/airflow:1.10.14-python3.8
- **Kernel** (e.g. `uname -a`): Linux host 5.4.0-62-generic #70-Ubuntu SMP Tue Jan 12 12:45:47 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux
- **Install tools**: Docker version 19.03.8, build afacb8b7f0
- **Others**:

**What happened**:
I created a DAG and set the doc_md property on the object but it isn't being rendered in the UI.

**What you expected to happen**:
I expected the markdown to be rendered in the UI

**How to reproduce it**:
Created a new container using the `airflow:1.10.14`, I have tried the following images with the same results.

-  airflow:1.10.14:image-python3.8
-  airflow:1.10.14:image-python3.7
-  airflow:1.10.12:image-python3.7
-  airflow:1.10.12:image-python3.7

```
dag_docs = """
## Pipeline

#### Purpose
This is a pipeline
"""

dag = DAG(
    'etl-get_from_api',
    default_args=default_args,
    description='A simple dag',
    schedule_interval=timedelta(days=1),
)
dag.doc_md = dag_docs

```

![image](https://user-images.githubusercontent.com/29732449/105004686-6b77d680-5a88-11eb-9e34-c8dd38b3fd10.png)

![image](https://user-images.githubusercontent.com/29732449/105004748-7af71f80-5a88-11eb-811c-11bc6a351c71.png)

I have also tried with using a doc-string to populate the doc_md as well as adding some text within the constructor.

```
dag = DAG(
    'etl-get_from_api',
    default_args=default_args,
    description='A simple dag',
    schedule_interval=timedelta(days=1),
    doc_md = "some text"
)
```
All of the different permutations I've tried seem to have the same result. The only thing I can change is the description, that appears to show up correctly.

**Anything else we need to know**:
I have tried multiple browsers (Firefox and Chrome) and I have also done an inspect on from both the graph view and the tree view from within the dag but I can't find any of the text within the page at all.

airflow/www/views.py
"""
    Create the list of edges needed to construct the Graph View.
    A special case is made if a TaskGroup is immediately upstream/downstream of another
    TaskGroup or task. Two dummy nodes named upstream_join_id and downstream_join_id are
    created for the TaskGroup. Instead of drawing an edge onto every task in the TaskGroup,
    all edges are directed onto the dummy nodes. This is to cut down the number of edges on
    the graph.
    For example: A DAG with TaskGroups group1 and group2:
        group1: task1, task2, task3
        group2: task4, task5, task6
    group2 is downstream of group1:
        group1 >> group2
    Edges to add (This avoids having to create edges between every task in group1 and group2):
        task1 >> downstream_join_id
        task2 >> downstream_join_id
        task3 >> downstream_join_id
        downstream_join_id >> upstream_join_id
        upstream_join_id >> task4
        upstream_join_id >> task5
        upstream_join_id >> task6
    """
    
    edges_to_add = set()
    
    edges_to_skip = set()
    task_group_map = dag.task_group.get_task_group_dict()
    def collect_edges(task_group):