## Step 1: Define the symbolic representation of the variables
The variables are 'grams of fat', 'milligrams of iron', and 'grams of fiber', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $5.49x_0 + 5.92x_1 + 3.77x_2$.

## Step 3: Define the constraints in symbolic notation
The constraints given are:
- Immune support index for $x_0$ is 4: $4x_0$
- Muscle growth index for $x_0$ is 27: $27x_0$
- Cardiovascular support index for $x_0$ is 29: $29x_0$
- Immune support index for $x_1$ is 15: $15x_1$
- Muscle growth index for $x_1$ is 13: $13x_1$
- Cardiovascular support index for $x_1$ is 5: $5x_1$
- Immune support index for $x_2$ is 8: $8x_2$
- Muscle growth index for $x_2$ is 3: $3x_2$
- Cardiovascular support index for $x_2$ is 28: $28x_2$

## 4: Express the constraints in algebraic form
The constraints are:
- $15x_1 + 8x_2 \geq 24$
- $4x_0 + 8x_2 \geq 23$
- $4x_0 + 15x_1 + 8x_2 \geq 23$
- $27x_0 + 13x_1 \geq 32$
- $13x_1 + 3x_2 \geq 36$
- $27x_0 + 3x_2 \geq 31$
- $27x_0 + 13x_1 + 3x_2 \geq 31$
- $29x_0 + 28x_2 \geq 50$
- $29x_0 + 5x_1 \geq 44$
- $29x_0 + 5x_1 + 28x_2 \geq 44$
- $-9x_1 + 8x_2 \geq 0$
- $2x_0 - x_2 \geq 0$
- $29x_0 + 28x_2 \leq 79$

## 5: Consider the variable bounds and types
- $x_0$ is continuous
- $x_1$ is integer
- $x_2$ is integer

## 6: Formulate the symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'grams of fat'), ('x1', 'milligrams of iron'), ('x2', 'grams of fiber')],
    'objective_function': '5.49*x0 + 5.92*x1 + 3.77*x2',
    'constraints': [
        '15*x1 + 8*x2 >= 24',
        '4*x0 + 8*x2 >= 23',
        '4*x0 + 15*x1 + 8*x2 >= 23',
        '27*x0 + 13*x1 >= 32',
        '13*x1 + 3*x2 >= 36',
        '27*x0 + 3*x2 >= 31',
        '27*x0 + 13*x1 + 3*x2 >= 31',
        '29*x0 + 28*x2 >= 50',
        '29*x0 + 5*x1 >= 44',
        '29*x0 + 5*x1 + 28*x2 >= 44',
        '-9*x1 + 8*x2 >= 0',
        '2*x0 - x2 >= 0',
        '29*x0 + 28*x2 <= 79'
    ]
}
```

## 7: Implement the problem in Gurobi
```python
import gurobi

def solve_optimization_problem():
    model = gurobi.Model()
    
    # Define variables
    x0 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="x0")  # grams of fat
    x1 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, vtype=gurobi.GRB.INTEGER, name="x1")  # milligrams of iron
    x2 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, vtype=gurobi.GRB.INTEGER, name="x2")  # grams of fiber

    # Objective function
    model.setObjective(5.49 * x0 + 5.92 * x1 + 3.77 * x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(15 * x1 + 8 * x2 >= 24, name="immune_support_index_constraint")
    model.addConstr(4 * x0 + 8 * x2 >= 23, name="immune_support_index_constraint_2")
    model.addConstr(4 * x0 + 15 * x1 + 8 * x2 >= 23, name="immune_support_index_constraint_3")
    model.addConstr(27 * x0 + 13 * x1 >= 32, name="muscle_growth_index_constraint")
    model.addConstr(13 * x1 + 3 * x2 >= 36, name="muscle_growth_index_constraint_2")
    model.addConstr(27 * x0 + 3 * x2 >= 31, name="muscle_growth_index_constraint_3")
    model.addConstr(27 * x0 + 13 * x1 + 3 * x2 >= 31, name="muscle_growth_index_constraint_4")
    model.addConstr(29 * x0 + 28 * x2 >= 50, name="cardiovascular_support_index_constraint")
    model.addConstr(29 * x0 + 5 * x1 >= 44, name="cardiovascular_support_index_constraint_2")
    model.addConstr(29 * x0 + 5 * x1 + 28 * x2 >= 44, name="cardiovascular_support_index_constraint_3")
    model.addConstr(-9 * x1 + 8 * x2 >= 0, name="linear_constraint")
    model.addConstr(2 * x0 - x2 >= 0, name="linear_constraint_2")
    model.addConstr(29 * x0 + 28 * x2 <= 79, name="linear_constraint_3")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Objective: {model.objval}")
        print(f"Grams of fat: {x0.varValue}")
        print(f"Milligrams of iron: {x1.varValue}")
        print(f"Grams of fiber: {x2.varValue}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```