## Problem Description and Symbolic Representation

The problem involves a bubble tea store that sells peach and mango flavored drinks. The goal is to maximize profit given certain constraints.

### Symbolic Variables:
- $x_1$ : Number of mango drinks sold
- $x_2$ : Number of peach drinks sold

### Objective Function:
The profit per mango drink is $2, and the profit per peach drink is $3. Therefore, the objective function to maximize profit is:
\[ \text{Maximize:} \quad 2x_1 + 3x_2 \]

### 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. Due to fruit shortages, they can make at most 120 mango drinks: $x_1 \leq 120$
5. Due to fruit shortages, they can make at most 70 peach drinks: $x_2 \leq 70$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'mango drinks'), ('x2', 'peach drinks')],
    'objective_function': '2*x1 + 3*x2',
    'constraints': [
        'x1 + x2 <= 150',
        'x1 >= 60',
        'x2 >= 40',
        'x1 <= 120',
        'x2 <= 70'
    ]
}
```

## Gurobi Code in Python

```python
import gurobi

def solve_bubble_tea_problem():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(lb=0, ub=120, name="mango_drinks")
    x2 = model.addVar(lb=0, ub=70, name="peach_drinks")

    # Objective function: Maximize 2*x1 + 3*x2
    model.setObjective(2*x1 + 3*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 <= 150, name="total_drinks")
    model.addConstr(x1 >= 60, name="min_mango_drinks")
    model.addConstr(x2 >= 40, name="min_peach_drinks")
    model.addConstr(x1 <= 120, name="max_mango_drinks")
    model.addConstr(x2 <= 70, name="max_peach_drinks")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Mango drinks: {x1.varValue}")
        print(f"Peach drinks: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_bubble_tea_problem()
```