Compare the MAE Vs R^2 output for Machine learning 10-fold CV
I am not sure about the scenario when the data is following the assumption of linearity.
But let's say the data has non-linearity, then this situation is possible considering the fact that squares of bigger values can easily cover-up the absolute sum of too many smaller values.
Let's see this plot below -
If we move from purple to the red line, MAE will shoot-up since we have too many points with increased MAE but SSE might come down because larger errors have been corrected. SSE coming down will improve the R2Score.
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 This was a rough calculation to get the intuition. You may extrapolate it.