## Step 1: Define the symbolic representation of the variables
The variables are 'grams of fiber' and 'milligrams of vitamin B3', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $3.32x_1^2 + 2.46x_1x_2 + 4.23x_2^2 + 7.99x_1 + 6.8x_2$.

## 3: Define the constraints in symbolic notation
The constraints are:
- $6x_1 + 20x_2 \geq 55$
- $6x_1 + 20x_2 \geq 55$ (same as the previous one)
- $x_1^2 + x_2^2 \geq 45$
- $x_1 + x_2 \geq 45$ is incorrect based on the provided attributes, the correct interpretation is $1x_1 + 1x_2 \geq 45$ is not directly provided, instead we have $1x_1 + 1x_2$ as part of the kidney support index, so $1x_1 + 1x_2 \geq 45$ seems to be a misinterpretation. The actual constraint from the problem statement is $x_1^2 + x_2^2 \geq 45$ for kidney support index.
- $9x_1 + 24x_2 \geq 47$
- $16x_1^2 + 26x_2^2 \geq 81$
- $16x_1 + 26x_2 \geq 81$
- $-4x_1 + x_2 \geq 0$
- $6x_1 + 20x_2 \leq 95$
- $1x_1 + 1x_2 \leq 138$
- $9x_1 + 24x_2 \leq 121$
- $16x_1 + 26x_2 \leq 105$
- $x_1$ is continuous
- $x_2$ is an integer

## 4: Correctly interpret the constraints based on the problem statement
Given the attributes:
- Digestive support index: $6x_1 + 20x_2$
- Kidney support index: $1x_1 + 1x_2$
- Cardiovascular support index: $9x_1 + 24x_2$
- Muscle growth index: $16x_1 + 26x_2$

And their respective constraints.

## 5: Formulate the problem in Gurobi code
```python
import gurobi

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

# Define the variables
x1 = m.addVar(name="grams_of_fiber", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY)
x2 = m.addVar(name="milligrams_of_vitamin_B3", lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, vtype=gurobi.GRB.INTEGER)

# Objective function
m.setObjective(3.32*x1**2 + 2.46*x1*x2 + 4.23*x2**2 + 7.99*x1 + 6.8*x2, gurobi.GRB.MINIMIZE)

# Constraints
m.addConstr(6*x1 + 20*x2 >= 55, name="digestive_support_index_min")
m.addConstr(x1**2 + x2**2 >= 45, name="kidney_support_index_min")
m.addConstr(9*x1 + 24*x2 >= 47, name="cardiovascular_support_index_min")
m.addConstr(16*x1**2 + 26*x2**2 >= 81, name="muscle_growth_index_min")
m.addConstr(16*x1 + 26*x2 >= 81, name="muscle_growth_index_min_linear")
m.addConstr(-4*x1 + x2 >= 0, name="linear_constraint")
m.addConstr(6*x1 + 20*x2 <= 95, name="digestive_support_index_max")
m.addConstr(1*x1 + 1*x2 <= 138, name="kidney_support_index_max")
m.addConstr(9*x1 + 24*x2 <= 121, name="cardiovascular_support_index_max")
m.addConstr(16*x1 + 26*x2 <= 105, name="muscle_growth_index_max")

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", m.objval)
    print("Grams of fiber: ", x1.varValue)
    print("Milligrams of vitamin B3: ", x2.varValue)
else:
    print("The model is infeasible")
```

## 6: Symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'grams of fiber'), ('x2', 'milligrams of vitamin B3')],
    'objective_function': '3.32*x1^2 + 2.46*x1*x2 + 4.23*x2^2 + 7.99*x1 + 6.8*x2',
    'constraints': [
        '6*x1 + 20*x2 >= 55',
        'x1^2 + x2^2 >= 45',
        '9*x1 + 24*x2 >= 47',
        '16*x1^2 + 26*x2^2 >= 81',
        '16*x1 + 26*x2 >= 81',
        '-4*x1 + x2 >= 0',
        '6*x1 + 20*x2 <= 95',
        '1*x1 + 1*x2 <= 138',
        '9*x1 + 24*x2 <= 121',
        '16*x1 + 26*x2 <= 105'
    ]
}
```