To solve Kevin's problem, we need to formulate a linear programming model that minimizes his cost while ensuring he meets his daily vitamin requirements. Let's denote:

- \(x\) as the number of Special Formula capsules Kevin buys.
- \(y\) as the number of One Daily capsules Kevin buys.

The objective function is to minimize the total cost, which can be represented as \(0.50x + 0.20y\), since each Special Formula capsule costs $0.50 and each One Daily capsule costs $0.20.

The constraints are:

1. Vitamin A requirement: \(4x + 3y \geq 25\) (since each Special Formula capsule contains 4 units of vitamin A and each One Daily capsule contains 3 units, and Kevin needs at least 25 units).
2. Vitamin B requirement: \(5x + 7y \geq 40\) (since each Special Formula capsule contains 5 units of vitamin B and each One Daily capsule contains 7 units, and Kevin needs at least 40 units).
3. Non-negativity constraints: \(x \geq 0\) and \(y \geq 0\), because Kevin cannot buy a negative number of capsules.

Now, let's translate this model into Gurobi code in Python:

```python
from gurobipy import *

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

# Add variables
x = m.addVar(vtype=GRB.CONTINUOUS, name="Special Formula", lb=0)
y = m.addVar(vtype=GRB.CONTINUOUS, name="One Daily", lb=0)

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

# Add constraints
m.addConstr(4*x + 3*y >= 25, "Vitamin_A")
m.addConstr(5*x + 7*y >= 40, "Vitamin_B")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Buy {x.x} Special Formula capsules")
    print(f"Buy {y.x} One Daily capsules")
    print(f"Total cost: ${m.objVal:.2f}")
else:
    print("No optimal solution found")

```