One other option with the tidyverse :

### Packages
library(purrr)
library(rvest)

### Data
x <-
  read_xml(
    "<Customers> <Customer> <ID> 01 </ID> <Name> Bla </Name> </Customer> <Customer> <ID> 02 </ID> </Customer> </Customers>"
  )

### Select all Customers elements
### For each one get the ID and the name of the person
### Merge the result in a dataframe
x %>%
  html_elements(xpath="//Customer") %>%
  map_df(~c(id= .x %>% html_element(xpath = ".//ID") %>%  html_text2(),
         name= .x %>% html_element(xpath = ".//Name") %>%  html_text2()))

Output :

# A tibble: 2 × 2
  id    name 
  <chr> <chr>
1 01    Bla  
2 02    NA   

Assuming every customer has an ID, you can go from there and use xml_find_first() to find the first sibling Name-node after an ID node... If there is none, the function returns NA.

# get all nodes with ID
ID.nodes <- xml_find_all(x, ".//ID")
# assuming Name comes after ID (if actually present)
# get following  sibling Name node
Name.nodes <- xml_find_first(ID.nodes, "./following-sibling::Name")

results

ID.nodes |> xml_text()
# [1] " 01 " " 02 "
Name.nodes |> xml_text()
# [1] " Bla " NA 

Given the end goal is a data frame, consider building a list of data frames from XML using find_all on Customer nodes, then iteratively call xml_children, xml_text, and xml_name on child nodes.

Then, compile all data frames with dplyr::bind_rows (or plyr::rbind_fill or user method) to fill NAs for missing columns. Below should handle all child nodes without hard-coding ID and Name.

library(dplyr)
library(xml2)

xml_str <- paste0(
  "<Customers>",
  "  <Customer>",
  "    <ID> 01 </ID>",
  "    <Name> Bla </Name>",
  "  </Customer>",
  "  <Customer>",
  "    <ID> 02 </ID>",
  "  </Customer>",
  "</Customers>"
)

doc <- read_xml(xml_str)

# RETRIEVE ALL NODES
recs <- xml2::xml_find_all(doc, "//Customer")

# BIND EACH CHILD TEXT AND NAME
df_list <- lapply(recs, function(r) {
  vals <- xml2::xml_children(r)
  
  df <- setNames(
    c(xml2::xml_text(vals) |> trimws()), 
    c(xml2::xml_name(vals))
  ) |> rbind() |> data.frame()
})

# COMBINE ALL DFS
customer_df <- dplyr::bind_rows(df_list)

customer_df
#   ID Name
# 1 01  Bla
# 2 02 <NA>