Decision trees for anomaly detection
I will try to refer to the points I have some opinion about:

&quot;What if it's not possible to label non-anomalous data to train the model?&quot;
In this case, you face unsupervised learning problem. there are plenty of reading material regarding this topic and plenty of approaches.
here is one for example:
https://towardsdatascience.com/unsupervised-learning-for-anomaly-detection-44c55a96b8c1

this article may be good as well:
https://www.researchgate.net/publication/224375498_Anomaly_Detection_by_Combining_Decision_Trees_and_Parametric_Densities

if you can distinguish anomalies by their distribution, it sounds that you do know the anomaly data, am I wrong?

the issue with defining anomalies lower than 5th quantile, that it means that 5 % of your data that examined by the leaf will be considered as anomaly, so you if run you algorithm frequently, there is a good chance you will see a lot of anomalies.
maybe try using confidence intervals.

From what I have read, decision trees are not the classic method for anomaly detection.
read about fraud detection, isolation trees, using SVM and there are more ways.