## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin B5', 'milligrams of magnesium', 'milligrams of vitamin B1', 'milligrams of vitamin E']. Let's denote them as $x_1, x_2, x_3, x_4$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ : milligrams of vitamin B5
- $x_2$ : milligrams of magnesium
- $x_3$ : milligrams of vitamin B1
- $x_4$ : milligrams of vitamin E

## 3: Define the objective function in symbolic notation
The objective function to maximize is: $8.94x_1^2 + 6.08x_1x_2 + 5.33x_2^2 + 3.3x_1$.

## 4: Define the constraints in symbolic notation
The constraints are:
- $10x_1 \leq 103$ (digestive support index for $x_1$ is 10, but it seems there was a misunderstanding in directly translating this. The actual constraint from the problem statement is $10 \leq 103$, which doesn't make sense. However, we have $10x_1$ as part of the index, so we consider $10$ as a given index value for $x_1$)
- $8x_2 \leq 103$ 
- $1x_3 \leq 103$
- $14x_4 \leq 103$
- $10x_1 + 8x_2 + 1x_3 \geq 15$
- $1x_3^2 + 14^2x_4^2 \leq 29$ seems incorrect based on the problem; correctly it should be $x_3^2 + x_4^2 \leq 29$
- $10x_1 + 8x_2 \leq 88$
- $8x_2 + 1x_3 \leq 40$
- $8x_2 + 14x_4 \leq 89$
- $8^2x_2^2 + 1^2x_3^2 + 14^2x_4^2 \leq 79$ seems incorrect; correctly it is $x_2^2 + x_3^2 + x_4^2 \leq 79$ or based on given data $64x_2^2 + x_3^2 + 196x_4^2 \leq 79$
- $10x_1 + 8x_2 + 14x_4 \leq 89$
- $10x_1 + 8x_2 + 1x_3 + 14x_4 \leq 89$

## 5: Correct and precise formulation of constraints based on given attributes
Given attributes: 
- $r0$: {'description': 'digestive support index', 'upper_bound': 103, 'x0': 10, 'x1': 8, 'x2': 1, 'x3': 14}

This implies:
- The digestive support index for $x_1$ is $10$
- The digestive support index for $x_2$ is $8$
- The digestive support index for $x_3$ is $1$
- The digestive support index for $x_4$ is $14$

And the constraints:
- $10x_1 + 8x_2 + x_3 \geq 15$
- $x_3^2 + x_4^2 \leq 29$
- $10x_1 + 8x_2 \leq 88$
- $8x_2 + x_3 \leq 40$
- $8x_2 + 14x_4 \leq 89$
- $64x_2^2 + x_3^2 + 196x_4^2 \leq 79$
- $10x_1 + 8x_2 + 14x_4 \leq 89$
- $10x_1 + 8x_2 + x_3 + 14x_4 \leq 89$

## 6: Formulate the optimization problem in Gurobi
```python
import gurobi

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

# Define the variables
x1 = model.addVar(name="x1", lb=0)  # milligrams of vitamin B5
x2 = model.addVar(name="x2", lb=0)  # milligrams of magnesium
x3 = model.addVar(name="x3", lb=0)  # milligrams of vitamin B1
x4 = model.addVar(name="x4", lb=0)  # milligrams of vitamin E

# Objective function
model.setObjective(8.94*x1**2 + 6.08*x1*x2 + 5.33*x2**2 + 3.3*x1, gurobi.GRB.MAXIMIZE)

# Constraints
model.addConstr(10*x1 + 8*x2 + x3 >= 15)
model.addConstr(x3**2 + x4**2 <= 29)
model.addConstr(10*x1 + 8*x2 <= 88)
model.addConstr(8*x2 + x3 <= 40)
model.addConstr(8*x2 + 14*x4 <= 89)
model.addConstr(64*x2**2 + x3**2 + 196*x4**2 <= 79)
model.addConstr(10*x1 + 8*x2 + 14*x4 <= 89)
model.addConstr(10*x1 + 8*x2 + x3 + 14*x4 <= 89)

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print("x1:", x1.varValue)
    print("x2:", x2.varValue)
    print("x3:", x3.varValue)
    print("x4:", x4.varValue)
    print("Objective:", model.objVal)
else:
    print("No optimal solution found.")
```

## 7: Symbolic Representation
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin B5'), ('x2', 'milligrams of magnesium'), ('x3', 'milligrams of vitamin B1'), ('x4', 'milligrams of vitamin E')],
    'objective_function': '8.94*x1^2 + 6.08*x1*x2 + 5.33*x2^2 + 3.3*x1',
    'constraints': [
        '10*x1 + 8*x2 + x3 >= 15',
        'x3^2 + x4^2 <= 29',
        '10*x1 + 8*x2 <= 88',
        '8*x2 + x3 <= 40',
        '8*x2 + 14*x4 <= 89',
        '64*x2^2 + x3^2 + 196*x4^2 <= 79',
        '10*x1 + 8*x2 + 14*x4 <= 89',
        '10*x1 + 8*x2 + x3 + 14*x4 <= 89'
    ]
}
```