To solve Kevin's vitamin supplement problem, we first need to translate the natural language description into a symbolic representation. 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 by the objective function:
\[0.50x_1 + 0.20x_2\]

The constraints are:
1. Vitamin A requirement: \(4x_1 + 3x_2 \geq 25\)
2. Vitamin B requirement: \(5x_1 + 7x_2 \geq 40\)
3. Non-negativity constraint for both variables since Kevin cannot buy a negative number of capsules: \(x_1, x_2 \geq 0\)

Thus, the symbolic representation in JSON format is:
```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']
}
```

To solve this problem using Gurobi in Python, we'll use the following code:
```python
from gurobipy import *

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

# Add variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="Special Formula capsules")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="One Daily capsules")

# Set the objective function
m.setObjective(0.50*x1 + 0.20*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(4*x1 + 3*x2 >= 25, "Vitamin A requirement")
m.addConstr(5*x1 + 7*x2 >= 40, "Vitamin B requirement")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Special Formula capsules: {x1.x}")
    print(f"One Daily capsules: {x2.x}")
    print(f"Total cost: ${0.50*x1.x + 0.20*x2.x:.2f}")
else:
    print("No optimal solution found")
```