## Step 1: Define the symbolic representation of the variables
The variables are 'kiwis', 'knishes', and 'bowls of instant ramen', which can be represented symbolically as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the natural language description into a symbolic representation
The objective function to maximize is $3x_1 + 4x_2 + 3x_3$.

## Step 3: Define the constraints in symbolic notation
The constraints given are:
- $2x_2 + 7x_3 \leq 65$
- $4x_1 + 7x_3 \leq 101$
- $4x_1 + 2x_2 + 7x_3 \leq 120$

## 4: Consider the non-integer amount of each variable
Since $x_1$, $x_2$, and $x_3$ can be non-integer (or fractional), we do not add any integrality constraints.

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

## 6: Implement the optimization problem using Gurobi
Now, let's implement this problem using Gurobi in Python.

```python
import gurobi

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

# Define the variables
x1 = model.addVar(name='kiwis', lb=0)  # Lower bound is 0, assuming we can't have negative quantities
x2 = model.addVar(name='knishes', lb=0)
x3 = model.addVar(name='bowls of instant ramen', lb=0)

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

# Add constraints
model.addConstr(2*x2 + 7*x3 <= 65)
model.addConstr(4*x1 + 7*x3 <= 101)
model.addConstr(4*x1 + 2*x2 + 7*x3 <= 120)

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', model.objVal)
    print('Kiwis: ', x1.varValue)
    print('Knishes: ', x2.varValue)
    print('Bowls of instant ramen: ', x3.varValue)
else:
    print('The model is infeasible')
```