## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of vitamin B2' and 'grams of fat'. Let's denote 'milligrams of vitamin B2' as $x_1$ and 'grams of fat' as $x_2$. The objective function to minimize is $3x_1 + 2x_2$.

## Step 2: List the constraints in symbolic notation
The constraints given are:
- The kidney support index for $x_1$ is 13, and for $x_2$ is 13.
- The cardiovascular support index for $x_1$ is 8, and for $x_2$ is 3.
- The total combined kidney support index must be 52 or more: $13x_1 + 13x_2 \geq 52$.
- The total combined cardiovascular support index must be 32 or more: $8x_1 + 3x_2 \geq 32$.
- The constraint $-3x_1 + 7x_2 \geq 0$.
- The total combined kidney support index must be 125 or less: $13x_1 + 13x_2 \leq 125$.
- The total combined cardiovascular support index must be 55 or less: $8x_1 + 3x_2 \leq 55$.

## 3: Define the symbolic representation
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'milligrams of vitamin B2'), ('x2', 'grams of fat')],
'objective_function': '3*x1 + 2*x2',
'constraints': [
    '13*x1 + 13*x2 >= 52',
    '8*x1 + 3*x2 >= 32',
    '-3*x1 + 7*x2 >= 0',
    '13*x1 + 13*x2 <= 125',
    '8*x1 + 3*x2 <= 55'
]
}
```

## 4: Convert the problem into Gurobi code
To solve this problem using Gurobi, we will use the Gurobi Python API.

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="milligrams_of_vitamin_B2", lb=0)  # No lower bound specified, assuming 0
    x2 = model.addVar(name="grams_of_fat", lb=0)  # No lower bound specified, assuming 0

    # Define the objective function
    model.setObjective(3*x1 + 2*x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(13*x1 + 13*x2 >= 52, name="kidney_support_index_min")
    model.addConstr(8*x1 + 3*x2 >= 32, name="cardiovascular_support_index_min")
    model.addConstr(-3*x1 + 7*x2 >= 0, name="linear_constraint")
    model.addConstr(13*x1 + 13*x2 <= 125, name="kidney_support_index_max")
    model.addConstr(8*x1 + 3*x2 <= 55, name="cardiovascular_support_index_max")

    # Optimize the model
    model.optimize()

    # Print the status of the model
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin B2: {x1.varValue}")
        print(f"Grams of fat: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    elif model.status == gurobi.GRB.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print("The model has a non-optimal status.")

solve_optimization_problem()
```