Interpreting the Root Mean Squared Error (RMSE)!

I read all about pros and cons of RMSE vs. other absolute errors namely mean absolute error (MAE). See the the following references:

Scenario: Let's say we have a regressor for predicting house prices with a MAE of 20.5\$and a RMSE of 24.5\$. Based on MAE, I can certainly interpret that the average difference between the predicted and the actual price is 20.5\$. How can I interpret RMSE? Can we still safely say the predicted and the actual price are off by 24.5\$ at the same time base on RMSE (upper-bound of prediction error)?

In the first medium post, it says:

RMSE does not describe average error alone and has other implications that are more difficult to tease out and understand.

It confuses me a little. And I could not find any reliable reference to also clearly state that one can safely interpret RSME as one does MAE. Is RMSE is simply a only mathematically more convenient for optimization etc., and we are better off with MAE for the interpretation?

Any detailed explanation is highly appreciated.

How can I interpret RMSE?

RMSE is exactly what's defined. \$24.5 is the square root of the average of squared differences between your prediction and your actual observation. Taking squared differences is more common than absolute difference in statistics, as you might have learnt from the classical linear regression.

It confuses me a little. And I could not find any reliable reference to also clearly state that one can safely interpret RSME as one does MAE. Is RMSE is simply a only mathematically more convenient for optimization etc., and we are better off with MAE for the interpretation?