## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the number of red bean toppings
- $x_2$ represents the number of pudding toppings

## Step 2: Translate the objective function into symbolic notation
The objective is to minimize the cost, which is $1.5x_1 + 2x_2$.

## 3: Translate the constraints into symbolic notation
The constraints are:
- $x_1 \leq 3$ (at most 3 red bean toppings)
- $1.5x_1 + 3x_2 \geq 7$ (at least 7 grams of sugar)
- $2.5x_1 + 1.2x_2 \geq 10$ (at least 10 grams of butter)
- $x_1, x_2 \geq 0$ (non-negativity constraint, as the number of toppings cannot be negative)

## 4: Express the problem in the required JSON format
```json
{
    'sym_variables': [('x1', 'red bean toppings'), ('x2', 'pudding toppings')],
    'objective_function': '1.5*x1 + 2*x2',
    'constraints': [
        'x1 <= 3',
        '1.5*x1 + 3*x2 >= 7',
        '2.5*x1 + 1.2*x2 >= 10',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Step 5: Implement the problem in Gurobi code
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(lb=0, name="red_bean_toppings")
    x2 = model.addVar(lb=0, name="pudding_toppings")

    # Objective function: minimize 1.5*x1 + 2*x2
    model.setObjective(1.5 * x1 + 2 * x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(x1 <= 3, name="red_bean_limit")
    model.addConstr(1.5 * x1 + 3 * x2 >= 7, name="sugar_requirement")
    model.addConstr(2.5 * x1 + 1.2 * x2 >= 10, name="butter_requirement")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Red bean toppings: {x1.varValue}")
        print(f"Pudding toppings: {x2.varValue}")
        print(f"Cost: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_milk_tea_problem()
```