To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. Let's denote the number of red bean toppings as $x_1$ and the number of pudding toppings as $x_2$. The objective is to minimize the cost, which can be represented as $1.5x_1 + 2x_2$. The constraints are:

- At most 3 red bean toppings: $x_1 \leq 3$
- At least 7 grams of sugar: $1.5x_1 + 3x_2 \geq 7$
- At least 10 grams of butter: $2.5x_1 + 1.2x_2 \geq 10$

The symbolic representation of the problem is:

```json
{
    'sym_variables': [('x1', 'number of red bean toppings'), ('x2', 'number of pudding toppings')],
    'objective_function': 'Minimize 1.5*x1 + 2*x2',
    'constraints': ['x1 <= 3', '1.5*x1 + 3*x2 >= 7', '2.5*x1 + 1.2*x2 >= 10']
}
```

To solve this problem using Gurobi in Python, we will use the following code:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, vtype=GRB.INTEGER, name="red_bean_toppings")
x2 = m.addVar(lb=0, vtype=GRB.INTEGER, name="pudding_toppings")

# Set the objective function
m.setObjective(1.5*x1 + 2*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(x1 <= 3, "max_red_bean")
m.addConstr(1.5*x1 + 3*x2 >= 7, "min_sugar")
m.addConstr(2.5*x1 + 1.2*x2 >= 10, "min_butter")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print("Red bean toppings:", x1.x)
    print("Pudding toppings:", x2.x)
    print("Minimum cost:", m.objVal)
else:
    print("No optimal solution found")
```