To solve this optimization problem, we need to translate the given natural language description into a mathematical model that can be implemented in Gurobi. The objective is to maximize the function $1.24x_0 + 3.93x_1 + 1.9x_2$, where $x_0$ represents the quantity of green beans, $x_1$ represents the number of ham sandwiches, and $x_2$ represents the amount of cornichons.

The constraints given in the problem can be summarized as follows:

1. Dollar cost constraint: $6x_0 + 15x_1 \leq 143$, $10x_0 + 16x_1 + x_2 \leq 131$ (not directly applicable but used to derive fiber constraints).
2. Minimum spend constraints:
   - $6x_0 + 15x_1 \geq 42$
   - $15x_1 + 4x_2 \geq 33$
   - $6x_0 + 15x_1 + 4x_2 \geq 34$
3. Maximum spend constraints:
   - $6x_0 + 15x_1 \leq 72$
   - $6x_0 + 15x_1 + 4x_2 \leq 72$
4. Fiber constraints:
   - $16x_1 + x_2 \leq 122$
   - $10x_0 + 16x_1 \leq 70$
   - $10x_0 + 16x_1 + x_2 \leq 70$

Given that $x_0$, $x_1$, and $x_2$ can be fractional, we do not need to specify integer constraints for any of these variables.

Here is the Gurobi code implementing this optimization problem:

```python
from gurobipy import *

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

# Create variables
x0 = m.addVar(name="green_beans", lb=0, ub=GRB.INFINITY)
x1 = m.addVar(name="ham_sandwiches", lb=0, ub=GRB.INFINITY)
x2 = m.addVar(name="cornichons", lb=0, ub=GRB.INFINITY)

# Set the objective function
m.setObjective(1.24*x0 + 3.93*x1 + 1.9*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(6*x0 + 15*x1 <= 143, name="dollar_cost_1")
m.addConstr(10*x0 + 16*x1 + x2 <= 131, name="fiber_total")
m.addConstr(6*x0 + 15*x1 >= 42, name="min_spend_green_ham")
m.addConstr(15*x1 + 4*x2 >= 33, name="min_spend_ham_corn")
m.addConstr(6*x0 + 15*x1 + 4*x2 >= 34, name="min_spend_all")
m.addConstr(6*x0 + 15*x1 <= 72, name="max_spend_green_ham")
m.addConstr(6*x0 + 15*x1 + 4*x2 <= 72, name="max_spend_all")
m.addConstr(16*x1 + x2 <= 122, name="fiber_ham_corn")
m.addConstr(10*x0 + 16*x1 <= 70, name="fiber_green_ham")
m.addConstr(10*x0 + 16*x1 + x2 <= 70, name="fiber_all")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print("green_beans:", x0.x)
    print("ham_sandwiches:", x1.x)
    print("cornichons:", x2.x)
else:
    print("No optimal solution found.")
```