To solve Daniel's problem, we first need to convert the natural language description into a symbolic representation of the optimization problem. Let's denote:

- $x_1$ as the amount (in grams) of Zeta supplementation used.
- $x_2$ as the amount (in grams) of Phi supplementation used.

The objective function aims to minimize the total cost of the supplementation, which can be represented as:
\[0.08x_1 + 0.18x_2\]

Given that Zeta supplementation consists of 15% iron and 20% vitamin A, and Phi supplementation consists of 20% iron and 45% vitamin A, we have the following constraints to ensure Daniel meets his minimum requirements:

1. For iron: $0.15x_1 + 0.20x_2 \geq 25$
2. For vitamin A: $0.20x_1 + 0.45x_2 \geq 40$

Additionally, since we cannot have negative amounts of supplementation, we also have:
\[x_1 \geq 0\]
\[x_2 \geq 0\]

Therefore, the symbolic representation of Daniel's problem is:

```json
{
  'sym_variables': [('x1', 'amount of Zeta supplementation in grams'), ('x2', 'amount of Phi supplementation in grams')],
  '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']
}
```

To find the optimal solution using Gurobi in Python, we can use the following code:

```python
from gurobipy import *

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

# Add variables to the model
x1 = m.addVar(lb=0, name="Zeta_Supplementation")
x2 = m.addVar(lb=0, name="Phi_Supplementation")

# Set the objective function
m.setObjective(0.08*x1 + 0.18*x2, GRB.MINIMIZE)

# Add constraints to the model
m.addConstr(0.15*x1 + 0.20*x2 >= 25, name="Iron_Requirement")
m.addConstr(0.20*x1 + 0.45*x2 >= 40, name="VitaminA_Requirement")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Zeta supplementation: {x1.x} grams")
    print(f"Phi supplementation: {x2.x} grams")
    print(f"Total cost: ${0.08*x1.x + 0.18*x2.x}")
else:
    print("No optimal solution found.")
```