## Step 1: Define the symbolic representation of the variables
The variables are 'tomatoes', 'apples', and 'sashimi', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : tomatoes
- $x_1$ : apples
- $x_2$ : sashimi

## Step 3: Define the objective function in symbolic notation
The objective function to minimize is: $6.14x_0^2 + 2.49x_0x_1 + 4.65x_0x_2 + 9.64x_1^2 + 6.75x_1x_2 + 4.93x_2^2 + 4.93x_0 + 2.95x_2$

## 4: List the constraints in symbolic notation
The constraints are:
- $2 \leq x_0$ (tastiness rating of tomatoes)
- $17x_0$ (cost of tomatoes, but we need to consider it in the context of other constraints for bounds)
- $7x_1$ (tastiness rating of apples)
- $15x_1$ (cost of apples)
- $12x_2$ (tastiness rating of sashimi)
- $15x_2$ (cost of sashimi)
- $x_0^2 + x_2^2 \geq 52$
- $2x_0 + 7x_1 \geq 54$
- $2x_0 + 7x_1 + 12x_2 \geq 54$
- $15x_1 + 15x_2 \geq 62$
- $17x_0 + 15x_1 \geq 33$
- $17x_0 + 15x_1 + 15x_2 \geq 33$
- $-2x_0^2 + 7x_2^2 \geq 0$
- $7^2x_1^2 + 12^2x_2^2 \leq 140$ or $49x_1^2 + 144x_2^2 \leq 140$
- $x_0^2 + x_2^2 \leq 100$
- $x_0$ is an integer
- $x_1$ is an integer
- $x_2$ can be fractional

## 5: Correct and simplify constraints for accurate representation
Correcting and simplifying:
- The tastiness ratings and costs are attributes, not constraints in the traditional sense but rather data.
- $x_0^2 + x_2^2 \geq 52$
- $2x_0 + 7x_1 \geq 54$
- $2x_0 + 7x_1 + 12x_2 \geq 54$
- $15x_1 + 15x_2 \geq 62$
- $17x_0 + 15x_1 \geq 33$
- $17x_0 + 15x_1 + 15x_2 \geq 33$
- $-2x_0^2 + 7x_2^2 \geq 0$
- $49x_1^2 + 144x_2^2 \leq 140$
- $x_0^2 + x_2^2 \leq 100$

## 6: Provide the symbolic representation in the required format
```json
{
    'sym_variables': [('x0', 'tomatoes'), ('x1', 'apples'), ('x2', 'sashimi')],
    'objective_function': '6.14x0^2 + 2.49x0x1 + 4.65x0x2 + 9.64x1^2 + 6.75x1x2 + 4.93x2^2 + 4.93x0 + 2.95x2',
    'constraints': [
        'x0^2 + x2^2 >= 52',
        '2x0 + 7x1 >= 54',
        '2x0 + 7x1 + 12x2 >= 54',
        '15x1 + 15x2 >= 62',
        '17x0 + 15x1 >= 33',
        '17x0 + 15x1 + 15x2 >= 33',
        '-2x0^2 + 7x2^2 >= 0',
        '49x1^2 + 144x2^2 <= 140',
        'x0^2 + x2^2 <= 100',
        'x0 is an integer',
        'x1 is an integer'
    ]
}
```

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

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

# Define the variables
x0 = m.addVar(name="tomatoes", vtype=gurobi.GRB.INTEGER)  # integer
x1 = m.addVar(name="apples", vtype=gurobi.GRB.INTEGER)  # integer
x2 = m.addVar(name="sashimi")  # continuous

# Define the objective function
m.setObjective(6.14*x0**2 + 2.49*x0*x1 + 4.65*x0*x2 + 9.64*x1**2 + 6.75*x1*x2 + 4.93*x2**2 + 4.93*x0 + 2.95*x2, gurobi.GRB.MINIMIZE)

# Add constraints
m.addConstr(x0**2 + x2**2 >= 52)
m.addConstr(2*x0 + 7*x1 >= 54)
m.addConstr(2*x0 + 7*x1 + 12*x2 >= 54)
m.addConstr(15*x1 + 15*x2 >= 62)
m.addConstr(17*x0 + 15*x1 >= 33)
m.addConstr(17*x0 + 15*x1 + 15*x2 >= 33)
m.addConstr(-2*x0**2 + 7*x2**2 >= 0)
m.addConstr(49*x1**2 + 144*x2**2 <= 140)
m.addConstr(x0**2 + x2**2 <= 100)

# Solve the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.Status.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Tomatoes: ", x0.varValue)
    print("Apples: ", x1.varValue)
    print("Sashimi: ", x2.varValue)
else:
    print("The model is infeasible")
```