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

Let's denote the amount of Zeta supplementation as $x_1$ grams and the amount of Phi supplementation as $x_2$ grams.

## Step 2: Convert the natural language description into a symbolic representation

The symbolic representation of the variables is:
- $x_1$: Zeta supplementation (in grams)
- $x_2$: Phi supplementation (in grams)

The objective is to minimize the cost, which is $0.08x_1 + 0.18x_2$.

The constraints based on the requirements are:
- Iron requirement: $0.15x_1 + 0.20x_2 \geq 25$
- Vitamin A requirement: $0.20x_1 + 0.45x_2 \geq 40$
- Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## 3: Express the problem in the required JSON format

```json
{
    'sym_variables': [('x1', 'Zeta supplementation'), ('x2', 'Phi supplementation')],
    'objective_function': '0.08*x1 + 0.18*x2',
    'constraints': [
        '0.15*x1 + 0.20*x2 >= 25',
        '0.20*x1 + 0.45*x2 >= 40',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 4: Translate the symbolic representation into Gurobi code

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="Zeta_supplementation", lb=0, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name="Phi_supplementation", lb=0, ub=gurobi.GRB.INFINITY)

    # Define the objective function
    model.setObjective(0.08 * x1 + 0.18 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(0.15 * x1 + 0.20 * x2 >= 25, name="Iron_requirement")
    model.addConstr(0.20 * x1 + 0.45 * x2 >= 40, name="Vitamin_A_requirement")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Zeta supplementation: {x1.varValue} grams")
        print(f"Phi supplementation: {x2.varValue} grams")
        print(f"Total cost: ${model.objVal:.2f}")
    else:
        print("No optimal solution found.")

solve_supplementation_problem()
```