what is the difference between Model drift and concept drift?

This is how I'd summarize it briefly:

Concept drift is when the actual rules of the world shift. Your machine learning model is trained to recognize spam email, and, over time, spammers begin to do things differently — different words, format, or approach. Now the pattern of what "spam" appears like has shifted. That is concept drift — your model's understanding of the world is out of date since the concept itself has shifted.

Model drift, conversely, is when your model's performance begins to decline. It could be due to concept drift, but it could also be for other reasons — perhaps data going into the model altered format, or there's noise, or even something in the pipeline has failed. You'll find predictions are incorrect, accuracy declines, and decisions no longer remain reliable.

So in short: concept drift is the cause; model drift is the effect. Let me know if you want to get examples specific to your use case.