How to create multiple plots for a specific time frame using DatetimeIndex in Python

I'm not sure if I fully understand the question and I used random data since I don't know where to find your dataset, but the principle should be the same since you want to separate by date/time and not actual values. I included comments in the code to explain what I'm trying to do.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import datetime

#Creating dataset (using random data for example)
t = pd.date_range('01-04-2021 09:00:00','31-12-2021 00:59:00',freq='1T')
df = pd.DataFrame(np.random.randint(999,size=(len(t),3)), index=t)
df.rename(columns={0: "open", 1: "low_close", 2: "high_close"}, inplace=True)

#Separating the data, first based on time, then based on date
interval = df.between_time('09:00','10:00') #Only contains data between 09:00 and 10:00
days_df = interval.groupby([interval.index.date]) #Make a seperate group (dataframe) for each day

#Make a separate image for each day (361 images)
for key,group in days_df:
    fig,ax = plt.subplots()
    ax.plot(group.open)

#Get data for a specific day and plot it
march19 = days_df.get_group(datetime.date(2021,3,19))
march19.plot()

I provide a similar answer, like Alex. Major problem is, that matplotlib cannot plot datetime.time objects, only datetime.datetime objects. Therefore I had to introduce a "dummy date". I strip the date from every timestamp and add for plotting the dummy date for each timestamp. This leads to an alignment of all daily closing prices.

You may remove the strptime code, as you have already datetime objects in your data frame. I had strings, because I imported test data from a test csv.

import datetime
import pandas as pd
import matplotlib.pyplot as plt

#read in data
df=pd.read_csv("test_csv.txt", delimiter=",")

#create two new columns based on "timestamp" column. Split date and time.
df["date"]=df["timestamp"].apply(lambda x: datetime.datetime.strptime(x, "%Y-%m-%d %H:%M:%S+00:00").date())
df["time"]=df["timestamp"].apply(lambda x: datetime.datetime.strptime(x, "%Y-%m-%d %H:%M:%S+00:00").time())
grp_trading_day=df.groupby("date")

#plot aligned data
dummy_date = datetime.date.today()
for trading_day, grp in grp_trading_day:
     x = grp["time"].apply(lambda x: datetime.datetime.combine(dummy_date, x))
     y = grp["close"]
     plt.plot(x, y)
plt.show()