To solve the given problem, we first need to convert the natural language description into a symbolic representation. Let's denote the number of cups of carrots as $x_1$ and the number of cups of spinach as $x_2$. The objective is to minimize the total cost, which can be represented as $5x_1 + 3x_2$. The constraints are:

- Biotin requirement: $x_1 + 2x_2 \geq 20$
- Folate requirement: $3x_1 + 1.5x_2 \geq 20$
- Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'cups of carrots'), ('x2', 'cups of spinach')],
    'objective_function': '5*x1 + 3*x2',
    'constraints': ['x1 + 2*x2 >= 20', '3*x1 + 1.5*x2 >= 20', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's implement this problem using Gurobi in Python:
```python
from gurobipy import *

# Create a model
m = Model("Optimal_Diet")

# Define the variables
x1 = m.addVar(lb=0, name="cups_of_carrots")
x2 = m.addVar(lb=0, name="cups_of_spinach")

# Set the objective function
m.setObjective(5*x1 + 3*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(x1 + 2*x2 >= 20, "biotin_requirement")
m.addConstr(3*x1 + 1.5*x2 >= 20, "folate_requirement")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Cups of carrots: {x1.x}")
    print(f"Cups of spinach: {x2.x}")
    print(f"Total cost: ${5*x1.x + 3*x2.x:.2f}")
else:
    print("No optimal solution found")
```