I subscribe the answer of ColorStatistics but let me add something. I have faced several problems like your exposed above and actually the key that bring us to solve them is to distinguish clearly between structural (causal) and regression (statistical) quantities. Unfortunately many econometrics books are unclear about that. This unclearness produce ambiguities and sometimes contradictions and mistakes. What you write/report is an example:
Greene [1] and Wooldridge [2] emphasize that in the standard multiple
linear regression model $${\bf y}=X{\bf b}+{\bf e}$$ a key assumption
is that $$E[{\bf e}|X]=E[{\bf e}].$$ Or, in other words, $X$ provide
no information about the expected value of ${\bf e}$. Provided that we
include an intercept in the model, this assumption will be equivalent
to $$E[{\bf e}|X]=E[{\bf e}]={\bf 0}.$$ Wooldridge call it zero
conditional mean assumption.
All the above is referred on linear regression model. The fact that the insert of the constant is considered as relevant assumpion underscore even more clearly that all above are written on regression equation. Those core assumptions are given at the start of the books and have consequence on all chapters ahead. No causal concepts/assumptions are given, at least not clearly, therefore no clear causal conclusions can be found. Unfortunately causal concepts are, or should be, one pillar in econometrics and in fact these, or something like these, appear in the books.
The zero conditional mean assumption for the error term, usually called exogeneity (even in Greene and Wooldridge), should be referred on a structural error, therefore a causal model should be involved.
Matter of fact is that in regression equation the zero mean for error and orthogonality between regressors and error holds by costruction and not by assumption; note that these fact remain true even at population level. Zero mean conditional independence between error and regressors can fail in regression equation. However if the exact conditional expectation form is linear the usual condition ($E[\epsilon|X]=0$) hold by construction too. In the case of regression on gaussian data even stochastic independence, between regressors and error, hold. However we have to note that not even this strong statistical condition is enough for achieve causal conclusions (see here:non stochastic regressors)
Even detail about contemporary observations or cross-observations can appear (see strict vs weak exogeneity). I faced all the possibility years ago for the first time. All of these thing tend to complicate the problems and obscure the key point that if we want to face the causal problems we need a causal model. Detail about statistical conditions cannot be enough, never. I surveyed many econometrics books in several edition. Is not easy to find authors/books the share precisely the same assumptions and terminology, several differences appears. However it seems me that there are no one econometrics book yet that use all the tools developed in causal inference literature, in most case no one is properly used. Read here: How would econometricians answer the objections and recommendations raised by Chen and Pearl (2013)?
I wrote something about this and related topic in this site. Among others:
linear causal model
Structural models and relationship (statistical associations)
Random Sampling: Weak and Strong Exogenity
Regression and causality in econometrics
What is the actual definition of endogeneity?
Zero conditional expectation of error in OLS regression
maybe these can help.
All I said can be useful for understand that is not easy to give a proper role to the zero conditional mean assumption as written above. In structural equations (causal models) it is crucial as causal assumption. At the other side, even if it can sound strange, in pure regression this assumption imply only restrictions on the joint distribution of the data; usually not a core question in econometrics. About the assumpions written as in the book of Gelman and Hill, note that in pure regression linearity assumption imply $E[\epsilon|X]=0$ also.