Your second example is not valid json. It is jsonl:

JSONL is a text-based format that uses the .jsonl file extension and is essentially the same as JSON format except that newline characters are used to delimit JSON data. It also goes by the name JSON Lines.

To deal with files like this you can read in each line as json, and then rbind() the results. A base R approach (requires at least R 4.2 for the _ placeholder):

library(jsonlite)
json_text <- readLines("tech.json", warn = FALSE, encoding = "UTF-8")

lapply(json_text, fromJSON) |>
    do.call(rbind, args = _) |>
    data.frame()

#   id   name  color price
# 1  1  Apple    red  0.99
# 2  2 Banana yellow   0.5
# 3  3 Orange orange  0.75

Alternatively, here's a dplyr approach:

library(dplyr)
lapply(json_text, fromJSON) |>
    bind_rows()

# # A tibble: 3 x 4
#      id name   color  price
#   <int> <chr>  <chr>  <dbl>
# 1     1 Apple  red     0.99
# 2     2 Banana yellow  0.5 
# 3     3 Orange orange  0.75

And for the sake of completeness here's the data.table way to do the same:

library(data.table)
lapply(json_text, fromJSON) |>
    rbindlist()
#       id   name  color price
#    <int> <char> <char> <num>
# 1:     1  Apple    red  0.99
# 2:     2 Banana yellow  0.50
# 3:     3 Orange orange  0.75

It’s possible to represent any JSON on a single line. This is difficult for humans to read, though, so we’re more familiar with the multi-line “pretty” version. For a storage-and-retrieval solution like S3, though, it’s much simpler to blindly read a whole line and return/store it than to have to parse a JSON line-by-line until the first opening { is finally closed (})

This is why it’s important to tell S3 how you’re storing and retrieving the files. From the docs you linked:

LINES means that each line in the input data contains a single JSON object. DOCUMENT means that a single JSON object can span multiple lines in the input. Using DOCUMENT might result in slower performance in some cases.