Why don’t we calculate the average of an entire given population instead of computing confidence interval to estimate the population mean? We compute confidence intervals to estimate the true population mean of either a sample (when population standard deviation is unknown) or an entire population (when population standard deviation is known). But I wonder why, if we are given an entire population, why don't we calculate the average of that population instead of computing confidence interval to estimate the population mean since we already have the entire population? I'm not a Math or Stat major so I apologize if my question does not sound "smart".

Update
So far neither the question nor any answer has provided a compelling example for the situation "we are given an entire population, so we don't need to do any inferring." I've been wondering whether this occurs outside a “Stat 101”-type of class and I found two examples in Improving Your Statistical Inferences, a great freely available resource by Daniël Lakens. The section Population vs. Sample is well worth the read by anyone who has the same question as the OP.
The first example is definitely a “Stat 101”-type of example, cute and without any relevance to a real-world problem: twelve people have walked on the moon, we know their height (or someone in NASA does), so we know the population average height of humans who have walked on the moon.
The second example is more interesting: [2] is a registy-based study of all children in Norway aged 5—17 between 2008 and 2016 (n = 1,354,393 children). The researchers investigate whether family income is linked to childhood mental illness. Even though technically the research have the entire population, they perform and report inferences, eg, "In the bottom 1% of parental income, 16.9% [95% confidence interval (CI): 15.6, 18.3] of boys had a mental disorder compared with 4.1% (95% CI: 3.3, 4.8) in the top 1%." A good example that there is much more to "inferring" in practice than the population mean which we can compute (if we have the population) or estimate (if we have a sample from the population).
References
[1] Daniël Lakens. Improving Your Statistical Inferences. Available online. 
[2] J. M. Kinge et al. Parental income and mental disorders in children and adolescents: prospective register-based study. International Journal of Epidemiology, 50(5):1615–1627, 2021. 

The statement "if we are given an entire population, why don't we calculate the average of that population" might be an unhelpful abstraction. For one, we average measurements, not samples or populations.
Statistics is the study of measurements and variation. If we can enumerate all individuals in a population, if we can measure the quantity of interest exactly from each individual and if those measurements and the population never change, then yes, there is no need for statistics because we have the complete data and there is no uncertainty to quantify. Also, this scenario sounds rather contrived.
Instead it might help to think of a non-trivial real-world example where the population is small & well understood, yet we still use statistics: forecasting the US presidential election, a process which involves the 50 US states (+ Washington, D.C.). We know how each state voted in the past and we want to predict how each state will vote in the next elections based on the historical voting patterns, past & current polls and any other relevant information. There is much uncertainty: we cannot measure precisely voting intent today and even if we could, the voting intent today is not exactly the same as the voting intent tomorrow. So even though we know the population of US states and it has only 50 members, to forecast elections, it can help to think of it as a dynamic collection of 50 states, one for each day until the election.
