Yes, there is an example: The iris dataset is nearly perfectly clustered by k-means regarding its three classes, while hdbscan is most likely not going to be able to recover those three classes. Of course you need to know that there are three classes.
However, I would argue that this task is not what clustering is about - it would be some sort of "unsupervised classification" task which is basically nonsense. However, an unfortunate amount of researcher are evaluating their papers like that (as in "trying if the clustering can recover labels"). The reason is simple: It is inherently difficult to evaluate unsupervised learning - I know, because I am a researcher of clustering myself. So this is an inherently invalid, but simple to understand "evaluation approach". If anyone is interested in more information on that, I can deliver, but I am not sure whether anyone care at this point.
Scientifically speaking there is no "good" or "bad" clustering technique. There are only different techniques following different definitions of what a "cluster" is in the first place. However, the definition that k-means follows is usually not the definition that you want - that is why k-means is usually not the method you want and thus k-means use is limited. The definition is very opinionated. In fact, it looks such that I am not even sure if I would call k-means a clustering method or rather a vector quantization method - as many others have called it as well.
And here we see a very useful application of k-means (and frankly the one I would use k-means for): To tessellate a space. Since k-means is also so very fast, it is very useful for some sort of "multidimensional histogram" or "pre-clustering" for speedups and these sort of things. Unfortunately this often means you want a large "k" and then k-means becomes slow (quadratic runtime), which kind of defeats the purpose. Fortunately, this is where dual trees come into play - they are able to make k-means fast even for large "k".
