Here's the Gurobi code to solve your optimization problem.  We define the variables, objective function, and constraints as described in your input.  Note that the objective function is quadratic, and we use Gurobi's `addQConstr` to add the constraint on the total tastiness rating being less than or equal to 96.

```python
import gurobipy as gp

# Create a new model
m = gp.Model("optimization_problem")

# Create variables
pizza = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="pizza")
strawberries = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="strawberries")
ravioli = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="ravioli")
chickens = m.addVar(lb=0, vtype=gp.GRB.INTEGER, name="chickens")

# Set objective function
obj = 8*pizza*pizza + 9*pizza*strawberries + 2*pizza*ravioli + 6*pizza*chickens + 5*strawberries*strawberries + 7*strawberries*ravioli + 5*strawberries*chickens + 3*ravioli*ravioli + 8*ravioli*chickens + 9*chickens*chickens + 9*pizza + 3*strawberries + 6*ravioli + 4*chickens
m.setObjective(obj, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(7*pizza + 4*strawberries + 9*ravioli >= 27, "tastiness_constr1")
m.addConstr(4*strawberries + 9*ravioli + 16*chickens >= 27, "tastiness_constr2")
m.addConstr(7*pizza + 4*strawberries + 16*chickens >= 27, "tastiness_constr3")
m.addConstr(7*pizza*pizza + 4*strawberries*strawberries + 9*ravioli*ravioli >= 39, "tastiness_constr4")
m.addConstr(4*strawberries + 9*ravioli + 16*chickens >= 39, "tastiness_constr5")
m.addConstr(7*pizza + 4*strawberries + 16*chickens >= 39, "tastiness_constr6")
m.addConstr(7*pizza + 4*strawberries + 9*ravioli >= 39, "tastiness_constr7")
m.addConstr(4*strawberries + 9*ravioli + 16*chickens >= 39, "tastiness_constr8")
m.addConstr(7*pizza + 4*strawberries + 16*chickens >= 39, "tastiness_constr9")

m.addConstr(pizza + 20*ravioli >= 19, "fat_constr1")
m.addConstr(3*strawberries + 20*ravioli >= 14, "fat_constr2")
m.addConstr(pizza + 3*strawberries >= 10, "fat_constr3")
m.addConstr(pizza + 8*chickens >= 9, "fat_constr4")
m.addConstr(3*strawberries + 20*ravioli + 8*chickens >= 15, "fat_constr5")
m.addConstr(pizza*pizza + 9*strawberries*strawberries + 400*ravioli*ravioli >= 15, "fat_constr6")
m.addConstr(9*strawberries*strawberries + 400*ravioli*ravioli + 64*chickens*chickens >= 16, "fat_constr7")
m.addConstr(pizza + 3*strawberries + 20*ravioli >= 16, "fat_constr8")


m.addConstr(15*strawberries + 17*ravioli + 16*chickens >= 33, "cost_constr1")
m.addConstr(9*pizza*pizza + 225*strawberries*strawberries + 289*ravioli*ravioli >= 33, "cost_constr2")
m.addConstr(15*strawberries + 17*ravioli + 16*chickens >= 33, "cost_constr3")
m.addConstr(3*pizza + 15*strawberries + 17*ravioli >= 33, "cost_constr4")

m.addConstr(7*pizza*pizza + 9*ravioli*ravioli <= 105, "tastiness_constr10")
m.addConstr(9*ravioli + 16*chickens <= 146, "tastiness_constr11")
m.addConstr(4*strawberries*strawberries + 16*chickens*chickens <= 68, "tastiness_constr12")
m.addConstr(4*strawberries + 9*ravioli + 16*chickens <= 119, "tastiness_constr13")
m.addConstr(7*pizza + 9*ravioli + 16*chickens <= 96, "tastiness_constr14")
m.addQConstr(7*pizza + 4*strawberries + 9*ravioli + 16*chickens <= 96, "tastiness_constr15")

m.addConstr(pizza + 3*strawberries <= 48, "fat_constr9")
m.addConstr(20*ravioli + 8*chickens <= 77, "fat_constr10")
m.addConstr(pizza*pizza + 400*ravioli*ravioli <= 57, "fat_constr11")
m.addConstr(pizza + 8*chickens <= 79, "fat_constr12")
m.addConstr(9*strawberries*strawberries + 400*ravioli*ravioli <= 28, "fat_constr13")
m.addConstr(pizza + 3*strawberries + 20*ravioli + 8*chickens <= 28, "fat_constr14")

m.addConstr(3*pizza + 15*strawberries <= 107, "cost_constr5")
m.addConstr(9*pizza*pizza + 289*ravioli*ravioli <= 100, "cost_constr6")
m.addConstr(289*ravioli*ravioli + 256*chickens*chickens <= 39, "cost_constr7")
m.addConstr(15*strawberries + 17*ravioli <= 55, "cost_constr8")
m.addConstr(15*strawberries + 16*chickens <= 47, "cost_constr9")
m.addConstr(3*pizza + 15*strawberries + 17*ravioli + 16*chickens <= 47, "cost_constr10")


# Add resource constraints based on the provided dictionary (optional)
resource_data = {'r0': {'description': 'tastiness rating', 'upper_bound': 158, 'x0': 7, 'x1': 4, 'x2': 9, 'x3': 16}, 'r1': {'description': 'grams of fat', 'upper_bound': 80, 'x0': 1, 'x1': 3, 'x2': 20, 'x3': 8}, 'r2': {'description': 'dollar cost', 'upper_bound': 141, 'x0': 3, 'x1': 15, 'x2': 17, 'x3': 16}}
for resource, data in resource_data.items():
    m.addConstr(data['x0']*pizza + data['x1']*strawberries + data['x2']*ravioli + data['x3']*chickens <= data['upper_bound'], resource)


# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print('Obj: %g' % m.objVal)
    for v in m.getVars():
        print('%s %g' % (v.varName, v.x))
elif m.status == gp.GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print("Optimization ended with status %d" % m.status)

```