BUG: Inconsistent resampling behaviour
- [x] I have checked that this issue has not already been reported.

- [x] I have confirmed this bug exists on the latest version of pandas.

- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.

---

Attached below is my code for extracting processing financial tick data from the MetaTrader5 application:

```python
def CollectTicks(Year, Month, Day, Hour, Minute, CurrencyPair, Digits, ResampleOffset = None):

    StartTime = datetime.datetime(Year, Month, Day, Hour, Minute).timestamp() #Convert the start time to epoch time

    EndTime = StartTime + 604800 #Add 1 week to the start time and set the result as the end time

    Ticks = mt5.copy_ticks_range(CurrencyPair, StartTime, EndTime, mt5.COPY_TICKS_ALL) #Retrieve the ticks from the start time until the end time

    BreakFlag = False #Set the break flag to false

    while(True):

        StartTime = EndTime #Set the prevous end time as the new start time

        EndTime = StartTime + 604800 #Add 1 week to the new start time and set the result as the new end time

        if(EndTime > time.time()): #If the end time is more than the current time

            EndTime = time.time() #Set the end time as the current time

            BreakFlag = True #Set the break flag to true

        NewTicks = mt5.copy_ticks_range(CurrencyPair, StartTime, EndTime, mt5.COPY_TICKS_ALL) #Retrive the new ticks with the new start time and new end time

        Ticks = np.hstack((Ticks, NewTicks)) #Cocatenate the newly retrieved ticks with the previously retrieved ticks

        if(BreakFlag == True): #If the break flag is set to true

            break #Exit the loop

    TickDataframe = pd.DataFrame(Ticks) #Covert the ticks array to a dataframe

    TickDataframe.drop(["time", "last", "volume", "flags", "volume_real"], axis = 1, inplace = True) #Drop the unwanted columns from the dataframe

    TickDataframe["time_msc"] = pd.to_datetime(TickDataframe["time_msc"], unit = "ms") #Convert the milisecond timestap to the epoch date time

    TickDataframe.set_index("time_msc", inplace = True) #Set the milisecond time column as the dataframe's index

    TickDataframe.round(Digits) #Round all values in the dataframe to the desired number of digits

    TickDataframe["mid"] = round(((TickDataframe["bid"] + TickDataframe["ask"]) / 2.0), Digits) #Generate the mid dataframe and round it to (Digits) figures

    if(ResampleOffset != None): #If the ResampleOffset input is not None

        CandleCloses = {} #Instantiate a dictionary

        CandleCloses["Bid Closes"] = TickDataframe["bid"].resample(ResampleOffset).ohlc().close.dropna().to_numpy() #Resample the bid ticks to create bid candles based on the resample offset and convert it into a numpy array

        CandleCloses["Ask Closes"] = TickDataframe["ask"].resample(ResampleOffset).ohlc().close.dropna().to_numpy() #Resample the ask ticks to create ask candles based on the resample offset and convert it into a numpy array

        CandleCloses["Mid Closes"] = TickDataframe["mid"].resample(ResampleOffset).ohlc().close.dropna().to_numpy() #Resample the mid ticks to create mid candles based on the resample offset and convert it into a numpy array

        return CandleCloses #Return the dictionary containing candle closes

    else: #If the ResampleOffset input is None

        return TickDataframe #Return the dataframe of ticks

```

#### Problem description

After resampling the collected tick data with the "1Min" offset, the resampled data for 2 different instruments have a different number of candles even when their time indexes cover the same date range.

#### Additional Observations

The other offsets do not have this inconsistent behaviour, the issue only happens with the "1Min" resample offset.

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.8.3.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : en
LOCALE           : English_Singapore.1252

pandas           : 1.0.5
numpy            : 1.18.5
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.1.1
setuptools       : 49.2.0.post20200714
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : 3.1.2
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.16.1
pandas_datareader: None
bs4              : 4.9.1
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.2.2
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : None
pyxlsb           : None
s3fs             : None
scipy            : 1.5.0
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : 0.50.1

</details>
