qcut not respecting precision values
#### Code Sample, a copy-pastable example if possible

```python
np.random.seed(42)
df = pd.DataFrame({"a": np.random.rand(20)})
for prec in range(1,10):
    print(f"Prec: {prec}, first interval: {pd.qcut(df['a'], q=10, precision=prec).cat.categories[0]}")

# Prec: 1, first interval: (0.011000000000000001, 0.15]
# Prec: 2, first interval: (0.011000000000000001, 0.15]
# Prec: 3, first interval: (0.0196, 0.146]
# Prec: 4, first interval: (0.02048, 0.1462]
# Prec: 5, first interval: (0.020574000000000002, 0.1462]
# Prec: 6, first interval: (0.020583499999999998, 0.146203]
# Prec: 7, first interval: (0.02058439, 0.1462034]
# Prec: 8, first interval: (0.020584483999999997, 0.14620343]
# Prec: 9, first interval: (0.0205844933, 0.14620343]
```
#### Problem description

Example above shows inconsistent behaviour of `qcut` function when changing the `precision` argument. I can potentially understand why the first 2 values returned what they did (maybe it is not possible to create enough intervals with low precision, but then error/warning should be raised, shouldn't it?), but output of precisions 5, 6 and 8 is rather confusing and causes issues in e.g. printing or plotting using labels.

This is not happening for all intervals as far as I can see, just the first one, see precisions 6 and 7 output below:

```python

Prec: 6, first interval: IntervalIndex([(0.020583499999999998, 0.146203], (0.146203, 0.176664], (0.176664, 0.203659], (0.203659, 0.299037], (0.299037, 0.403243], (0.403243, 0.554317], (0.554317, 0.633202], (0.633202, 0.752084], (0.752084, 0.87463], (0.87463, 0.96991]],
              closed='right',
              dtype='interval[float64]')
Prec: 7, first interval: IntervalIndex([(0.02058439, 0.1462034], (0.1462034, 0.1766637], (0.1766637, 0.2036587], (0.2036587, 0.299037], (0.299037, 0.4032426], (0.4032426, 0.5543173], (0.5543173, 0.6332023], (0.6332023, 0.7520837], (0.7520837, 0.87463], (0.87463, 0.9699099]],
              closed='right',
              dtype='interval[float64]')

```

I have checked issues for "qcut precision", but none of the existing issues seems to cover this.

#### Expected Output

```python
# Prec: 1, first interval: (0.01, 0.2]
# Prec: 2, first interval: (0.011, 0.15]
# Prec: 3, first interval: (0.0196, 0.146]
# Prec: 4, first interval: (0.02048, 0.1462]
# Prec: 5, first interval: (0.020574, 0.1462]
# Prec: 6, first interval: (0.0205835, 0.146203]
# Prec: 7, first interval: (0.02058439, 0.1462034]
# Prec: 8, first interval: (0.020584484, 0.14620343]
# Prec: 9, first interval: (0.0205844933, 0.14620343]
```

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.10.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.14.165-102.185.amzn1.x86_64
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.0.1
numpy            : 1.18.1
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 45.2.0.post20200210
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : 1.2.7
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : 2.8.4 (dt dec pq3 ext lo64)
jinja2           : 2.11.1
IPython          : 7.12.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.2
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 0.15.1
pytables         : None
pytest           : None
pyxlsb           : None
s3fs             : 0.4.0
scipy            : 1.4.1
sqlalchemy       : 1.3.13
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : 1.2.7
numba            : None

</details>
