In an M1 environment, malloc():unaligned tcache chunk detected error occurs when many requests are sent using firebase-emulator inside the Docker.

### [REQUIRED] Environment info

**firebase-tools:**
firebase-tools: 11.9.0

**Platform:**
masOS 12.5 (M1 Pro)

### [REQUIRED] Test case

```
FROM ubuntu:22.04
RUN apt-get update && apt-get install -y apt-transport-https ca-certificates curl gnupg && \
curl -sLf --retry 3 --tlsv1.2 --proto "=https" 'https://packages.doppler.com/public/cli/gpg.DE2A7741A397C129.key' | apt-key add - && \
echo "deb https://packages.doppler.com/public/cli/deb/debian any-version main" | tee /etc/apt/sources.list.d/doppler-cli.list && \
apt-get update && \
apt-get -y install doppler

RUN apt-get install -y curl wget openjdk-17-jre-headless

RUN curl -sL https://deb.nodesource.com/setup_14.x | bash - \
&& apt install -y nodejs \
&& apt install lsof

RUN apt-get -y install openssl

RUN npm install -g firebase-tools

RUN npm install -g yarn

```

### [REQUIRED] Steps to reproduce

Hasura remote schema <-> This occurs when many requests (graphql queries) are sent after connecting to functions within the firebase emulator.

Hasura version hasura/graphql-engine:v2.9.0.cli-migrations-v3 It is.

### [REQUIRED] Expected behavior

Hasura remote-schema<-> The firebase-emulator (in the docker) does not seem to respond normally.

Starting the firebase-emulator from the CLI makes it smooth, but using Docker's M1 image (ubuntu) causes problems.

(It works with AMD64/ubuntu images, but it is too slow to run AMD64 images on M1)

### [REQUIRED] Actual behavior

```
Beginning execution of \"graphql\"
malloc():unaligned tcache chunk detected <- (debug mode)
```

Start by reproducing the failure with the supplied Ubuntu Docker setup, firebase-tools 11.9.0, an M1 environment, and Hasura GraphQL queries. Compare the Docker behavior with running the Firebase emulator directly and with the AMD64 image. Done means repeated requests complete without the malloc error and the emulator responds normally.

Written by the indexing model from the issue text.