To solve this optimization problem, we first need to translate the natural language description into a symbolic representation. Let's define:

- $x_1$ as the number of mango drinks sold,
- $x_2$ as the number of peach drinks sold.

The objective function is to maximize profit, which can be represented algebraically as:
\[ 2x_1 + 3x_2 \]

Given the constraints:
1. The store can make at most 150 drinks total: $x_1 + x_2 \leq 150$,
2. They must sell at least 60 mango drinks: $x_1 \geq 60$,
3. They must sell at least 40 peach drinks: $x_2 \geq 40$,
4. They can make at most 120 mango drinks: $x_1 \leq 120$,
5. They can make at most 70 peach drinks: $x_2 \leq 70$.

So, the symbolic representation of the problem is:
```json
{
  'sym_variables': [('x1', 'number of mango drinks'), ('x2', 'number of peach drinks')],
  'objective_function': '2*x1 + 3*x2',
  'constraints': ['x1 + x2 <= 150', 'x1 >= 60', 'x2 >= 40', 'x1 <= 120', 'x2 <= 70']
}
```

To find the solution using Gurobi in Python, we will write the following code:

```python
from gurobipy import *

# Create a new model
m = Model("Bubble_Tea_Optimization")

# Define variables
x1 = m.addVar(lb=60, ub=120, vtype=GRB.INTEGER, name="Mango_Drinks")
x2 = m.addVar(lb=40, ub=70, vtype=GRB.INTEGER, name="Peach_Drinks")

# Set the objective function
m.setObjective(2*x1 + 3*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 <= 150, "Total_Drinks_Constraint")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Mango Drinks: {x1.x}")
    print(f"Peach Drinks: {x2.x}")
    print(f"Maximum Profit: {m.objVal}")
else:
    print("No optimal solution found")
```