## Step 1: Define the symbolic representation of the problem

Let's denote:
- $x_1$ as the number of Special Formula capsules
- $x_2$ as the number of One Daily capsules

The objective is to minimize the cost, which can be represented as $0.50x_1 + 0.20x_2$.

## Step 2: Define the constraints based on the problem description

The constraints are:
- Vitamin A: $4x_1 + 3x_2 \geq 25$
- Vitamin B: $5x_1 + 7x_2 \geq 40$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## 3: Convert the problem into a symbolic representation

```json
{
    'sym_variables': [('x1', 'Special Formula capsules'), ('x2', 'One Daily capsules')],
    'objective_function': '0.50*x1 + 0.20*x2',
    'constraints': [
        '4*x1 + 3*x2 >= 25',
        '5*x1 + 7*x2 >= 40',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Step 4: Implement the problem in Gurobi code

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="Special Formula capsules", lb=0, vtype=gurobi.GRB.CONTINUOUS)
    x2 = model.addVar(name="One Daily capsules", lb=0, vtype=gurobi.GRB.CONTINUOUS)

    # Define the objective function
    model.setObjective(0.50 * x1 + 0.20 * x2, gurobi.GRB.MINIMIZE)

    # Define the constraints
    model.addConstr(4 * x1 + 3 * x2 >= 25, name="Vitamin A constraint")
    model.addConstr(5 * x1 + 7 * x2 >= 40, name="Vitamin B constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: x1 = {x1.varValue}, x2 = {x2.varValue}")
        print(f"Minimum cost: {model.objVal}")
    else:
        print("The problem is infeasible")

solve_optimization_problem()
```