[R] write_dataset() - supplying partition name as character not schema results in it being missing from dataset

### Describe the bug, including details regarding any error messages, version, and platform.

#37658 added the capability for the partition variable to be part of the CSV dataset, but it only works when the partition is supplied as a schema object and not a column name.

``` r
library(arrow)
library(dplyr)
tf <- tempfile()
dir.create(tf)
csv_dir <- file.path(tf, "csv_dir")
parquet_dir <- file.path(tf, "parquet_dir")
dir.create(csv_dir)
dir.create(parquet_dir)
write_dataset(group_by(mtcars[1:16,], cyl), csv_dir, format = "csv")
write_dataset(group_by(mtcars[17:32,], cyl), parquet_dir, format = "parquet")
csv_dataset <- open_dataset(csv_dir, format = "csv")
parquet_dataset <- open_dataset(parquet_dir, format = "parquet")

# CSV parse error
open_dataset(
csv_dir,
format = "csv",
schema = schema(parquet_dataset),
skip_rows = 1,
partitioning = "cyl"
) %>%
collect()
#> Error in `compute.Dataset()`:
#> ! Invalid: Could not open CSV input source '/tmp/RtmpoQQt2q/file25f3a27580770/csv_dir/cyl=4/part-0.csv': Invalid: CSV parse error: Row #2: Expected 11 columns, got 10: 22.8,108,93,3.85,2.32,18.61,1,1,4,1
#> Backtrace:
#> ▆
#> 1. ├─... %>% collect()
#> 2. ├─dplyr::collect(.)
#> 3. └─arrow:::collect.Dataset(.)
#> 4. ├─arrow:::collect.ArrowTabular(compute.Dataset(x), as_data_frame)
#> 5. └─arrow:::compute.Dataset(x)
#> 6. └─base::tryCatch(...)
#> 7. └─base (local) tryCatchList(expr, classes, parentenv, handlers)
#> 8. └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
#> 9. └─value[[3L]](cond)
#> 10. └─arrow:::augment_io_error_msg(e, call, schema = schema())
#> 11. └─rlang::abort(msg, call = call)

# this works fine
open_dataset(
csv_dir,
format = "csv",
schema = schema(parquet_dataset),
skip_rows = 1,
partitioning = schema("cyl" = int32())
) %>%
collect()
#> # A tibble: 16 × 11
#> mpg disp hp drat wt qsec vs am gear carb cyl
#>
#> 1 22.8 108 93 3.85 2.32 18.6 1 1 4 1 4
#> 2 24.4 147. 62 3.69 3.19 20 1 0 4 2 4
#> 3 22.8 141. 95 3.92 3.15 22.9 1 0 4 2 4
#> 4 21 160 110 3.9 2.62 16.5 0 1 4 4 6
#> 5 21 160 110 3.9 2.88 17.0 0 1 4 4 6
#> 6 21.4 258 110 3.08 3.22 19.4 1 0 3 1 6
#> 7 18.1 225 105 2.76 3.46 20.2 1 0 3 1 6
#> 8 19.2 168. 123 3.92 3.44 18.3 1 0 4 4 6
#> 9 17.8 168. 123 3.92 3.44 18.9 1 0 4 4 6
#> 10 18.7 360 175 3.15 3.44 17.0 0 0 3 2 8
#> 11 14.3 360 245 3.21 3.57 15.8 0 0 3 4 8
#> 12 16.4 276. 180 3.07 4.07 17.4 0 0 3 3 8
#> 13 17.3 276. 180 3.07 3.73 17.6 0 0 3 3 8
#> 14 15.2 276. 180 3.07 3.78 18 0 0 3 3 8
#> 15 10.4 472 205 2.93 5.25 18.0 0 0 3 4 8
#> 16 10.4 460 215 3 5.42 17.8 0 0 3 4 8

# both of these work
open_dataset(
parquet_dir,
schema = schema(parquet_dataset),
partitioning = "cyl"
) %>%
collect()
#> # A tibble: 16 × 11
#> mpg disp hp drat wt qsec vs am gear carb cyl
#>
#> 1 32.4 78.7 66 4.08 2.2 19.5 1 1 4 1 4
#> 2 30.4 75.7 52 4.93 1.62 18.5 1 1 4 2 4
#> 3 33.9 71.1 65 4.22 1.84 19.9 1 1 4 1 4
#> 4 21.5 120. 97 3.7 2.46 20.0 1 0 3 1 4
#> 5 27.3 79 66 4.08 1.94 18.9 1 1 4 1 4
#> 6 26 120. 91 4.43 2.14 16.7 0 1 5 2 4
#> 7 30.4 95.1 113 3.77 1.51 16.9 1 1 5 2 4
#> 8 21.4 121 109 4.11 2.78 18.6 1 1 4 2 4
#> 9 19.7 145 175 3.62 2.77 15.5 0 1 5 6 6
#> 10 14.7 440 230 3.23 5.34 17.4 0 0 3 4 8
#> 11 15.5 318 150 2.76 3.52 16.9 0 0 3 2 8
#> 12 15.2 304 150 3.15 3.44 17.3 0 0 3 2 8
#> 13 13.3 350 245 3.73 3.84 15.4 0 0 3 4 8
#> 14 19.2 400 175 3.08 3.84 17.0 0 0 3 2 8
#> 15 15.8 351 264 4.22 3.17 14.5 0 1 5 4 8
#> 16 15 301 335 3.54 3.57 14.6 0 1 5 8 8

open_dataset(
parquet_dir,
schema = schema(parquet_dataset),
partitioning = schema("cyl" = int32())
) %>%
collect()
#> # A tibble: 16 × 11
#> mpg disp hp drat wt qsec vs am gear carb cyl
#>
#> 1 32.4 78.7 66 4.08 2.2 19.5 1 1 4 1 4
#> 2 30.4 75.7 52 4.93 1.62 18.5 1 1 4 2 4
#> 3 33.9 71.1 65 4.22 1.84 19.9 1 1 4 1 4
#> 4 21.5 120. 97 3.7 2.46 20.0 1 0 3 1 4
#> 5 27.3 79 66 4.08 1.94 18.9 1 1 4 1 4
#> 6 26 120. 91 4.43 2.14 16.7 0 1 5 2 4
#> 7 30.4 95.1 113 3.77 1.51 16.9 1 1 5 2 4
#> 8 21.4 121 109 4.11 2.78 18.6 1 1 4 2 4
#> 9 19.7 145 175 3.62 2.77 15.5 0 1 5 6 6
#> 10 14.7 440 230 3.23 5.34 17.4 0 0 3 4 8
#> 11 15.5 318 150 2.76 3.52 16.9 0 0 3 2 8
#> 12 15.2 304 150 3.15 3.44 17.3 0 0 3 2 8
#> 13 13.3 350 245 3.73 3.84 15.4 0 0 3 4 8
#> 14 19.2 400 175 3.08 3.84 17.0 0 0 3 2 8
#> 15 15.8 351 264 4.22 3.17 14.5 0 1 5 4 8
#> 16 15 301 335 3.54 3.57 14.6 0 1 5 8 8
```

^{Created on 2024-01-09 with [reprex v2.0.2](https://reprex.tidyverse.org)}

### Component(s)

R