Confusion matrix and ROC AUC curves are not in sync
The ROC curve is built by taking different decision thresholds, and should be built using the predict_proba of your estimator.  In particular, in your multiclass example, the ROC is using the values 0,1,2 as a rank-ordering!  So there are four thresholds, the one between 0 and 1 being the most important here: there, you declare all of the samples the model predicts as being class 0 to be negative, and all others as positive, giving you the complementary point in the ROC-space.

Instead, you should have

fpr,tpr, thres =  roc_curve(y_test, model.predict_proba(x_test)[:,0], pos_label=0)
# or 
fpr,tpr, thres =  roc_curve(y_test==0, model.predict_proba(x_test)[:,0])