DataFrameReader of Spark not able to recognize the very first quote character, while custom unicode quote character is used

I have encountered a strange behaviour recently, while reading a CSV file using DataFrameReader of org.apache.spark.sql package (Spark version 2.3.2). Here is my spark read code snippet.

val sepChar = "\u00C7" // Ç
val quoteChar = "\u1E1C" // Ḝ
val escapeChar = "\u1E1D" // ḝ
val inputCsvFile = "<some-local-windows-path>
input_ab.csv"

val readDF = sparkSession.read.option("sep", sepChar)
.option("encoding", encoding.toUpperCase)
.option("quote", quoteChar)
.option("escape", escapeChar)
.option("header", "false")
.option("multiLine", "true")
.csv(inputCsvFile)
readDF.cache()
readDF.show(20, false)

Due to some awful data, I'm forced to use some unicode characters as sep character, quote character, escape character instead of default ones. Below is my input sample data.

Ḝ1ḜÇḜsmithḜÇḜ5Ḝ
Ḝ2ḜÇḜdousonḜÇḜ6Ḝ
Ḝ3ḜÇḜsr,tendulkarḜÇḜ10Ḝ

Here Ç is field separator, Ḝ is quote character and all the fields values are wrapped with this custom quote character.

The problem I'm getting is, the first occurance of the quote character is not getting recognized by Spark somehow. I tried with any charcter other than Unicode like ` ~ X (alphabet X just for a testing scenario), even default quote (") as well. It works fine in all the scenarios except when Unicode is used as quote character. The first occurance of the Unicode quote character is coming as some non printable character �� , hence the wrap end quote character of the first field of first record is getting included in data.

Here is the output of df show.

+---+------------+-----+
|id |name |class|
+---+------------+-----+
|��1Ḝ |smith |5 |
|2 |douson |6 |
|3 |sr,tendulkar|10 |
+---+------------+-----+

It happens only for the first field of the very first record. Other quote characters in this file is being read as expected without any issues. When I keep an extra empty record at the top of the file, i.e., simply a new line (\n) at the very first line, the issue doesn't occur. Even, that empty row is not being considered as an empty record in df as well. Thus my problem gets solved. But this manipulation cannot be done in the production and hence it is an issue to be bothered about.

I feel, this is a bug. If it is not, kindly let me know the way to process the same without getting this issue; or else kindly provide a fix at the earliest. Thanks in advance.

Best Regards,
Mrinal