I'll be talking somewhat loosely here, but hopefully this can give a way of thinking about it.
Once you observe it, a sample is a collection of numbers, not a collection of random variables (with corresponding distribution(s)).
To the extent that you can say a sample 'has a distribution', its empirical distribution function is discrete, it's a step function. The random variables that the sample values are realizations of, of course could have some continuous distribution and those may well be seen as being independent and identically distributed. But in the limit as the sample size of a simple random sample goes to $\infty$, the ecdf does approach the population distribution you were sampling from.
"If a population is normally distributed, a sample randomly chosen from it must be normally distributed."
The $n$ variables making up a simple random sample of some population distribution (/data generating process) each have that population distribution; $X_1\sim F$, $X_2\sim F$,..., $X_n\sim F$. Once you realize these random variables (actually observe their values), they're each just a number.
"If a sample randomly chosen from a certain population is normally distributed, the population must be normally distributed."
If your simple random sample is the collection of variates $X_1, X_2, ..., X_n$, then each of them has the same distribution as the population; so if you know the distribution of the variable $X_k$, you know the population distribution; they're the same object.
Once you realize that set of random variables, and literally have the numbers $x_1, x_2, ..., x_n$, even if you make the assumption that they're an ideal case of a simple random sample of some population, you simply cannot determine what distribution they came from.
You can be quite sure that they didn't come from some distributions. e.g. if you're observing necessarily positive quantities (like weights of apples), you can be absolutely certain -- even before you see any of the numbers -- that they're not a sample from an actual normal distribution.
Indeed the chance that real data actually represent a simple random sample from any simple-form distribution is fantastically unlikely (that very specific mathematical shape... exactly?).
Not that this is important in practice; distributions are abstractions/idealizations -- literally 'models' of the thing they're modelling, not the thing itself. The question is generally not 'is this model literally the truth' but more 'is this model useful for our present purpose'.
So it may make perfect sense to model apple weights by a normal distribution, but that's not the real thing; the normal distribution is a convenient abstraction, a tool.
That is, can I believe that its population is normally distributed, if I find a certain sample is normally distributed?
You literally can't "find a certain sample is normally distributed".
I presume you're hinting at having tested normality, but if so, you're misunderstanding what that can tell you.
Note first that the actual null hypothesis in that case is that the population is normally distributed. Not about the sample; you're seeing if the sample is inconsistent with that hypothesis about the population.
If you test normality, you may well fail to reject normality, but you would also fail to reject an infinite number of non-normal distributions at the same time, and at least some of those would have a better chance to produce your sample* than whatever distribution you decide to test for.
* or a higher chance to produce a set of values within some small $\delta x$ of each observed value if you want an event with an actual non-zero probability, rather than comparing relative likelihoods say.