Should I scale or normalise my dataset before clustering?
As often in Machine Learning, there is no clear answer. In fact, both are valid options [1, p. 116]. However, for k-means min-max-scaling is usually used in practice [2]. So min-max-scaling would be the default choice and it's what I'd recommend. But as so often you can simply try both and see which provides better results (i.e. better internal cluster validation measures, such as the Silhouette Index).
References:
[1] https://dbs.ifi.uni-heidelberg.de/files/Team/eschubert/lectures/KDDClusterAnalysis17-screen.pdf
[2] https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152173