## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin C', 'grams of fiber', 'milligrams of vitamin B3']. 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:
- $x_1$ = 'milligrams of vitamin C'
- $x_2$ = 'grams of fiber'
- $x_3$ = 'milligrams of vitamin B3'

## Step 3: Define the objective function in symbolic notation
The objective function to minimize is $6x_1 + 6x_2 + 2x_3$.

## Step 4: List the constraints in symbolic notation
The constraints are:
1. $4x_1 \leq 60$
2. $5x_2 \leq 60$
3. $1x_3 \leq 60$
4. $4x_1 + 5x_2 \geq 14$
5. $5x_2 + 1x_3 \geq 7$
6. $4x_1 + 5x_2 + 1x_3 \geq 17$
7. $4x_1 + 5x_2 + 1x_3 \geq 17$
8. $-7x_2 + 7x_3 \geq 0$
9. $-5x_1 + 1x_3 \geq 0$
10. $-1x_1 + 10x_2 \geq 0$
11. $5x_2 + 1x_3 \leq 26$

## 5: Consider the variable bounds and types
- $x_1$ is a continuous variable.
- $x_2$ is a continuous variable.
- $x_3$ is an integer variable.

## 6: Write down the symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'milligrams of vitamin C'), ('x2', 'grams of fiber'), ('x3', 'milligrams of vitamin B3')],
'objective_function': '6*x1 + 6*x2 + 2*x3',
'constraints': [
    '4*x1 <= 60',
    '5*x2 <= 60',
    'x3 <= 60',
    '4*x1 + 5*x2 >= 14',
    '5*x2 + x3 >= 7',
    '4*x1 + 5*x2 + x3 >= 17',
    '-7*x2 + 7*x3 >= 0',
    '-5*x1 + x3 >= 0',
    '-x1 + 10*x2 >= 0',
    '5*x2 + x3 <= 26'
]
}
```

## 7: Implement the optimization problem using Gurobi
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="milligrams of vitamin C")
    x2 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="grams of fiber")
    x3 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, vtype=gurobi.GRB.INTEGER, name="milligrams of vitamin B3")

    # Objective function
    model.setObjective(6 * x1 + 6 * x2 + 2 * x3, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(4 * x1 <= 60)
    model.addConstr(5 * x2 <= 60)
    model.addConstr(x3 <= 60)
    model.addConstr(4 * x1 + 5 * x2 >= 14)
    model.addConstr(5 * x2 + x3 >= 7)
    model.addConstr(4 * x1 + 5 * x2 + x3 >= 17)
    model.addConstr(-7 * x2 + 7 * x3 >= 0)
    model.addConstr(-5 * x1 + x3 >= 0)
    model.addConstr(-x1 + 10 * x2 >= 0)
    model.addConstr(5 * x2 + x3 <= 26)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin C: {x1.varValue}")
        print(f"Grams of fiber: {x2.varValue}")
        print(f"Milligrams of vitamin B3: {x3.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```