Can I calculate the variance in this way? If not, what should I do?
Confirming whether your method correctly calculates the standard error is beyond the scope of the user support team. I can however direct you to the variables that you will need to implement the sampling error formula provided in section 3.9 of the documentation that you shared.
In the equation, e indexes the 32 states of Mexico (GEO1_MX), h indexes the design stratum (MX2020H_EST_D), and i indexes the Primary Sampling Unit (i.e., UPM). These sample design variables are provided by IPUMS International as source variables and can be found by selecting Source Variables from the top of the variable selector page. Source variables have not been harmonized across time; they are sample-specific and the corresponding variable for each quarter of the Mexico ENOE that you plan to analyze needs to be added to your data cart. The correct weight to use for person-level estimates is PERWT and the weight for household-level estimates is HHWT. Weights are referred to as expansion factors (factor de expansión) in the documentation.
Thank you Ivan, you are very warm-hearted.
Here is an another question : If a person whose age > 14 and EMPSTAT == 0, can I categorize him as NILF (not being in the labor force, such as he is currently serving in the military) ?
In other words, is it correct for me to define people with age < 15 or EMPSTAT == 0 as NILF?
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EMPSTAT = 0 identifies persons who are not-in-universe (i.e., NIU) for the questions regarding employment status, meaning that the survey does not inquire into their employment status. The universe statement for EMPSTAT in the Mexico ENOE labor survey is present persons age 12+, indicating that the survey does not ask for the employment status of children under the age of 12 or for persons who do not reside in the household. Instead, these persons are assigned EMPSTAT = 0. When filtering records to those with AGE => 12 and RESIDENT = 1 (present resident), almost all records (99.9%+) will have a value for EMPSTAT > 0.
The ENOE is a rotating panel survey where each household is sampled five times over the course of five quarters. In the first interview, a household roster is created that lists every person who resided in the household at the time of the initial interview. Records for residents who are then noted as absent from the sampled household during any of the subsequent quarters are preserved in the data (these are the persons with RESIDENT = 2). However, their employment status is not recorded and instead set as NIU. There are a number of reasons someone might leave the household including moving in with a new partner, moving away from their previous partner, or passing away. You can tabulate the reasons any particular person left with the source variable MX2020H_CS_AD_MOT (reason for absence of habitual resident). You can also find the interview number (1-5) with the source variable MX2020H_N_ENT (interview number).
Leaving the household does not mean that the respondent is NILF, but only that they are no longer being sampled as part of the ENOE. You might treat those who left the household for work as being in the labor force and those who moved to study as leaving the labor force (although students can still be working). Aside from these cases and persons who have passed away, it is not possible to discern whether a non-resident is in the labor force or NILF. Additionally, none of the reasons for leaving the household specifically cite military service. I am also unable to find any variables that would identify respondents who are sampled while in military service.
Thank you Ivan, your response was very helpful. 
Hi Ivan, may I ask you a question? Is there any variable to show whether the person is employed formally or informally in Mexico Census 2020? In Mexico Survey 2020 Q1, it is MX2020H_EMP_PPAL, but I can’t find it in Mexico Census 2020.