How to interpret metrics of a model after scaling the data
I solved this issue with the help of @Franziska W. Thanks!

I currently reverse transform y_test and y_pred and then calculate the metrics as following:

# qt_y is a QuantileTransformer instance, gb is a GradientBoostingRegressor
y_pred = gb.predict(X_test)
inv_y_pred = qt_y.inverse_transform(y_pred.reshape(-1, 1))
inv_y_test = qt_y.inverse_transform(y_test.reshape(-1, 1))

mse_test = mean_squared_error(inv_y_test, inv_y_pred)
print("RMSE on Test:", np.sqrt(mse_test))
print("MSE on Test:", mse_test)

mae_test = mean_absolute_error(inv_y_test, inv_y_pred)
print("MAE on Test:", mae_test)