Why ROC value area under curve of two models is different whereas accuracy, precision, recall, f1-score and confusion matrix is same
Those scorings looks strange for me, but beside that you must remember that F1, accuracy, confusion matrix, etc depends on the chosen threshold, while AUC is threshold-independent (it is an integral over all of the thresholds from 0 to 1).

Your models return some probabilities of being a member of class 1. If you choose to label by '1' only those, which have probability higher than 99% (this is a threshold) then you get a recall at almost 100% and a precision near 0%.