To tackle this problem, we first need to understand and translate the natural language description into a symbolic representation. This involves defining the variables, the objective function, and the constraints in terms of these variables.

Let's denote:
- \(x_1\) as the amount of kiwis,
- \(x_2\) as the quantity of knishes,
- \(x_3\) as the quantity of bowls of instant ramen.

The objective function to maximize is: \(3x_1 + 4x_2 + 3x_3\).

The constraints given are:
1. \(4x_1 + 2x_2 + 7x_3 \leq 120\) (Total grams of protein from all items),
2. \(2x_2 + 7x_3 \leq 65\) (Total grams of protein from knishes and bowls of instant ramen),
3. \(4x_1 + 7x_3 \leq 101\) (Total grams of protein from kiwis and bowls of instant ramen),
4. \(4x_1 + 2x_2 + 7x_3 \leq 101\) (Total combined grams of protein from all items, which seems to be a repetition or slight modification of the first constraint but will be considered as is).

All variables (\(x_1\), \(x_2\), \(x_3\)) can take non-integer values.

Thus, the symbolic representation of the problem is:
```json
{
  'sym_variables': [('x1', 'kiwis'), ('x2', 'knishes'), ('x3', 'bowls of instant ramen')],
  'objective_function': '3*x1 + 4*x2 + 3*x3',
  'constraints': [
    '4*x1 + 2*x2 + 7*x3 <= 120',
    '2*x2 + 7*x3 <= 65',
    '4*x1 + 7*x3 <= 101',
    '4*x1 + 2*x2 + 7*x3 <= 101'
  ]
}
```

Now, let's implement this optimization problem using Gurobi in Python:
```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Define the variables
x1 = m.addVar(name="kiwis", lb=0)  # Amount of kiwis
x2 = m.addVar(name="knishes", lb=0)  # Quantity of knishes
x3 = m.addVar(name="bowls_of_instant_ramen", lb=0)  # Quantity of bowls of instant ramen

# Set the objective function
m.setObjective(3*x1 + 4*x2 + 3*x3, GRB.MAXIMIZE)

# Add constraints
m.addConstr(4*x1 + 2*x2 + 7*x3 <= 120, name="Total_protein_from_all_items")
m.addConstr(2*x2 + 7*x3 <= 65, name="Protein_from_knishes_and_ramen")
m.addConstr(4*x1 + 7*x3 <= 101, name="Protein_from_kiwis_and_ramen")
m.addConstr(4*x1 + 2*x2 + 7*x3 <= 101, name="Total_protein_limit")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Kiwis: {x1.x}")
    print(f"Knishes: {x2.x}")
    print(f"Bowls of instant ramen: {x3.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```