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

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ for 'milligrams of vitamin B1'
- $x_2$ for 'milligrams of vitamin C'
- $x_3$ for 'milligrams of potassium'

## 3: Define the objective function in symbolic notation
The objective function to minimize is $4x_1 + 5x_2 + 5x_3$.

## 4: Define the constraints in symbolic notation
The constraints are:
- $8x_1 \geq 8 \cdot 8$ is not correct, it is just $8$ as a digestive support index for $x_1$,
- $19x_2$ for 'milligrams of vitamin C',
- $6x_3$ for 'milligrams of potassium'.
Correctly, they are:
- $8x_1$ has a digestive support index of $8$,
- $19x_2$ has a digestive support index of $19$,
- $6x_3$ has a digestive support index of $6$.
The constraints given are:
- $8x_1 + 19x_2 \geq 55$,
- $19x_2 + 6x_3 \geq 41$,
- $8x_1 + 19x_2 + 6x_3 \geq 41$,
- $x_1 - 8x_3 \geq 0$,
- $19x_2 + 6x_3 \leq 295$,
- $8x_1 + 6x_3 \leq 151$,
- $8x_1 + 19x_2 + 6x_3 \leq 287$.

## 5: Create a symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'milligrams of vitamin B1'), ('x2', 'milligrams of vitamin C'), ('x3', 'milligrams of potassium')],
'objective_function': '4*x1 + 5*x2 + 5*x3',
'constraints': [
    '8*x1 + 19*x2 >= 55',
    '19*x2 + 6*x3 >= 41',
    '8*x1 + 19*x2 + 6*x3 >= 41',
    'x1 - 8*x3 >= 0',
    '19*x2 + 6*x3 <= 295',
    '8*x1 + 6*x3 <= 151',
    '8*x1 + 19*x2 + 6*x3 <= 287'
]
}
```

## 6: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name='x1', lb=-gurobi.GRB.INFINITY)  # milligrams of vitamin B1
    x2 = model.addVar(name='x2', lb=-gurobi.GRB.INFINITY)  # milligrams of vitamin C
    x3 = model.addVar(name='x3', lb=-gurobi.GRB.INFINITY)  # milligrams of potassium

    # Define the objective function
    model.setObjective(4 * x1 + 5 * x2 + 5 * x3, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(8 * x1 + 19 * x2 >= 55)
    model.addConstr(19 * x2 + 6 * x3 >= 41)
    model.addConstr(8 * x1 + 19 * x2 + 6 * x3 >= 41)
    model.addConstr(x1 - 8 * x3 >= 0)
    model.addConstr(19 * x2 + 6 * x3 <= 295)
    model.addConstr(8 * x1 + 6 * x3 <= 151)
    model.addConstr(8 * x1 + 19 * x2 + 6 * x3 <= 287)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Objective: ', model.objval)
        print('x1: ', x1.x)
        print('x2: ', x2.x)
        print('x3: ', x3.x)
    else:
        print('The model is infeasible')

solve_optimization_problem()
```