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

Let's define the symbolic variables:
- $x_1$ represents the number of cups of carrots
- $x_2$ represents the number of cups of spinach

The objective is to minimize the cost, which is $5x_1 + 3x_2$.

The constraints are:
- Biotin requirement: $1x_1 + 2x_2 \geq 20$
- Folate requirement: $3x_1 + 1.5x_2 \geq 20$
- Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## Step 2: Convert the problem into a Gurobi-compatible format

We will use the Gurobi library in Python to solve this linear programming problem.

## Step 3: Write down the problem in JSON format

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

## Step 4: Implement the problem in Gurobi Python

```python
import gurobi

# Create a new model
model = gurobi.Model()

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

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

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

# Optimize the model
model.optimize()

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