Can Micro-Average Roc Auc Score be larger than Class Roc Auc Scores
In a binary problem there's no reason to average the ROC (or any other metric). Normally micro and macro performances are used to obtain a single performance value based on individual binary classification measures in the case of multiclass classification.
So here what happens is this:

The ROC curves for the two classes are the mirror of each other (using the top-left bottom-right diagonal as symmetry axis), because they represent exactly the same points but with the positive and negative classes swapped.
The macro-average curve is the average of both curves, which doesn't make a lot of sense since both already have the same shape. This is why all the AUC values are identical for macro, class 0 and class 1.
The micro-average ROC is the weighted average, so it's made mostly of the majority class (around 75%): since most points in the majority class are correctly predicted as the majority class, the performance looks much better. This is related to the fact that the micro-average F1-score is equal to accuracy, although in the ROC I'm not sure how the points are weighted exactly.

In my opinion, this is a good illustration of why ROC curves should be used very carefully (or not at all) in a multiclass setting. ROC curves are meant for binary (soft) classification, they are useful and interpretable in this context but not necessarily in another context.
In general it also illustrates that it's not because something can be calculated that the resulting value makes sense ;)