Potential bug in reading SAS files with CHAR (RLE) compression and many repeated characters
Hi,

I think I ran into a bug in the RLE decompression implementation.

**Short description**:
String fields with more than 32 repeated consecutive characters are ~be~ cropped at 32 and next fields will spill over corrupting the whole dataframe.

**Example**:

example.csv with fields of length 50
```
long_string_field1,long_string_field2,long_string_field3
"00000000000000000000000000000000000000000000000000","11111111111111111111111111111111111111111111111111","aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
```

Create a CHAR compressed sas7bdat file (system encoding is set to latin1)
```sas
options compress=char;
proc import datafile="path\example.csv"
        out=your_lib.example
        dbms=csv
        replace;
        getnames=yes;
run;
```

```python
import pandas as pd
example = pd.read_sas("./example.sas7bdat", encoding="latin1")
```

This is what you get:
```python
>>> example['long_string_field1'].values[0]
'00000000000000000000000000000000  111111111111111111'
```
```python
>>> example['long_string_field2'].values[0]
'11111111111111  aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa'
```
```python
>>> example['long_string_field3'].values[0]
nan
```
There are a couple of interesting points:
1. Exactly 32 characters are read/written from each field. Originals had 50 characters.
2. The dataframe fields are filled up to the 50 limit (+ 2 additional spaces between the sources of the fields. In case of consecutive integers) with spillovers from next fields.
3. This only happens if one uses the CHAR compression. (RLE)
4. This only happens if you have repeated consecutive characters.
5. The `sas7bdat` package works fine.

#### DISCLAIMER
I would never use any of the things above out of my free will. Sadly, this is an actual case I keep running into when having to deal with SAS... 😢 

#### Output of ``pd.show_versions()``

<details>

```
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.9.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : None.None

pandas           : 0.25.3
numpy            : 1.17.5
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 19.3.1
setuptools       : 45.1.0.post20200119
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : 2.8.4 (dt dec pq3 ext lo64)
jinja2           : 2.10.3
IPython          : 7.11.1
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 0.15.1
pytables         : None
s3fs             : None
scipy            : None
sqlalchemy       : 1.3.12
tables           : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
```
</details>
