Hello
First, thank you for posting this question. I was unaware of gendered colors in data visualization before I read your post. I think you highlight important aspects when it comes to choosing color schemes in data visualization. There is a tradeoff between clarity and gender-free color schemes since those stereotypes exist in our society. While a clear and intuitive visualization is one of the top priorities for chart designers, I personally think that creating gender neutral/unpolarizing content is even more important. The use of pink and blue to illustrate data on women and men respectively is unnecessarily reinforcing gendered stereotypes. However, this tradeoff does not have to persist. Large news organizations have started using different colors when visualizing gender specific data such as data on wage inequality (An alternative to pink & blue: Colors for gender data). Some of these organizations used colors that were completely different from blue and pink such as orange and green. Others simply used pink for male and blue for female. This, however, might lead to misinterpretation of the data. Therefore, I would suggest you use completely different colors for your visualization. Regarding guidelines on colors associated with gender, multiple articles (such as A few color options for representing gender — Data Viz Today) refer to green and dark purple (#1b909a, #7900f1). They are the colors of the “Votes for women” campaign in the U.K. The colors of a female rights movement are very well suited in my opinion, as the movement emphisizes and advocates equality rather than highlighting differences between genders and I also think they are less polarizing while maintaining an intuitive interpretation for gender data. Adapting such a guideline would increase understandability and clarity of data visualization while not advocating gender roles.
To answer your second question, “should renouncing the use of gendered colours be considered a visualisation guideline, not just a personal principle for those who choose to adopt it?”, I think we should renounce the use of gendered colors in visualization guidelines. Yeung & Wong wrote an interesting paper on the impact of gender labels and gender-neutral colors on performance. They found that “having any gender labels could widen the gender gap in play performance” (Yeung & Wong, 2018).
A last point I would like to address is that I personally think the guidelines on colors for gender data visualization should include more than just two colors, as there are many people that cannot identify with the conventional two genders. Providing guidelines for inclusive data visualization might be key to avoid future disagreements.
I hope my answer is of use to you.