## Step 1: Define the symbolic representation of the variables
The variables are 'protein bars', 'steaks', 'ravioli', and 'knishes', which can be represented symbolically as $x_1, x_2, x_3, x_4$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $3.09x_1 + 9.24x_2 + 3.08x_3 + 1.7x_4$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
1. $8x_1 + 4x_2 + 3x_3 + 2x_4 \geq 14$ (total protein from all sources),
2. $8x_1 + 3x_3 \geq 14$ (total protein from protein bars and ravioli),
3. $3x_3 + 2x_4 \geq 28$ (total protein from ravioli and knishes),
4. $9x_1 - 5x_4 \geq 0$ (relationship between protein bars and knishes),
5. $-10x_3 + 10x_4 \geq 0$ (relationship between ravioli and knishes).

## 4: Determine the symbolic representation of the problem
The symbolic representation is:
- Variables: $(x_1, 'protein bars'), (x_2, 'steaks'), (x_3, 'ravioli'), (x_4, 'knishes')$
- Objective function: $3.09x_1 + 9.24x_2 + 3.08x_3 + 1.7x_4$
- Constraints:
1. $8x_1 + 4x_2 + 3x_3 + 2x_4 \geq 14$
2. $8x_1 + 3x_3 \geq 14$
3. $3x_3 + 2x_4 \geq 28$
4. $9x_1 - 5x_4 \geq 0$
5. $-10x_3 + 10x_4 \geq 0$

## 5: Implement the problem in Gurobi
We will use Gurobi to solve this linear programming problem.

```python
import gurobi as gp

# Define the model
m = gp.Model()

# Define the variables
x1 = m.addVar(name="protein_bars", lb=0)  # No upper bound for protein bars, steaks, ravioli, knishes as they can be fractional
x2 = m.addVar(name="steaks", lb=0)
x3 = m.addVar(name="ravioli", lb=0)
x4 = m.addVar(name="knishes", lb=0)

# Define the objective function
m.setObjective(3.09*x1 + 9.24*x2 + 3.08*x3 + 1.7*x4, gp.GRB.MINIMIZE)

# Define the constraints
m.addConstr(8*x1 + 4*x2 + 3*x3 + 2*x4 >= 14, name="total_protein")
m.addConstr(8*x1 + 3*x3 >= 14, name="protein_bars_ravioli")
m.addConstr(3*x3 + 2*x4 >= 28, name="ravioli_knishes")
m.addConstr(9*x1 - 5*x4 >= 0, name="protein_bars_knishes")
m.addConstr(-10*x3 + 10*x4 >= 0, name="ravioli_knishes_relationship")

# Optimize the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Protein bars: {x1.varValue}")
    print(f"Steaks: {x2.varValue}")
    print(f"Ravioli: {x3.varValue}")
    print(f"Knishes: {x4.varValue}")
    print(f"Objective: {m.objVal}")
else:
    print("No optimal solution found.")
```

## 6: Provide the symbolic representation in the required format
```json
{
    'sym_variables': [('x1', 'protein bars'), ('x2', 'steaks'), ('x3', 'ravioli'), ('x4', 'knishes')],
    'objective_function': '3.09*x1 + 9.24*x2 + 3.08*x3 + 1.7*x4',
    'constraints': [
        '8*x1 + 4*x2 + 3*x3 + 2*x4 >= 14',
        '8*x1 + 3*x3 >= 14',
        '3*x3 + 2*x4 >= 28',
        '9*x1 - 5*x4 >= 0',
        '-10*x3 + 10*x4 >= 0'
    ]
}
```