You need to define a distance function that yields the desirable output.
Usually it will be enough if you can construct it such that d(a,b)for your purpose.
Doc2vec vectors are a bit tricky because they have so many dimensions, and a very strange geometry. It doesn't even seem to be clear whether cosine or Euclidean is to be used on these vectors...
Either way, you have to carefully balance the different features. In the other answer, minmax or stddev scaling was proposed. On one hand, that likely ruins the doc2vec properties, on the other hand this will but much more weight on the word vectors than on the other attributes.
For DBSCAN, you can also follow the "Generalized DBSCAN" approach. Here, the idea is just to define different thresholds for different features. Then neighbors must satisfy all thresholds. I.e. have doc2vec cosine less than A, and other features' distance less than B.
This is likely easier than balancing them as factors inside a single distance function.
But nothing saves you from weighting different feature (sets} carefully.